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1.
Urban trees provide numerous ecosystem goods and services by providing shade, habitat for wildlife, removal of air pollutants and the removal and storage of atmospheric CO2. Carbon removal services provided by Canadian urban trees have previously been assessed using an IPCC 2006 guidelines approach based on the percentage of urban area covered by tree canopy (UTC) for the 2012 time period (Pasher et al., 2014). That work however provided only a single point in time assessment of the national scale UTC and carbon removal services. The research undertaken for this study was a continuation of this earlier work focusing on a 1990 national scale UTC assessment and carbon sequestration estimates for 1990. UTC estimates for 1990 were developed using a point sampling approach with circa 1990 air photos covering a large portion of Canadian urban areas. In total almost 179,000 points were sampled for the 1990 time period, reassessing 83% of the points used for the previous 2012 assessment. Based on the urban area boundary layers for 1991 and 2011, Canada’s urban areas grew by an estimated 6% for this time period. Most of this growth occurred through conversion of agricultural and forested lands to urban. At the national scale the UTC for 1990 was estimated to be 27.6%, as compared to the 2012 UTC estimate of 26.1%, the difference between estimates for the two time periods fell within the uncertainty range. Carbon removal estimates based on the UTC estimates were also very similar for the two dates with 660.2 kt C removed in 1990 and 662.8 kt C removed in 2012. It was noted that urban development in the Prairie regions resulted in an increase in tree cover as compared to the pre-conversion agricultural and natural landscapes and also that in most urban areas across the country UTC increases through time as tree cover matured in newly developed urban areas. These two assessments provide a time series of urban trees for 22 year time period, which will be useful for further studies and analysis. 相似文献
2.
Urban tree canopy cover (UTC) is a simple, and common, measure of urban forest resource. Urban infill development is likely to lead to losses in UTC under private tenure, at a time when local governments are setting ambitious targets to increase UTC overall. Simple, statistically rigorous methods are required to benchmark and track change in UTC, whilst identifying which land-use types or tenures experience change.We estimated UTC in six Melbourne suburbs in 2010 and 2015 by randomly sampling 2000 points across public land, public streetscapes and private land. We were able to detect a net change in UTC of <2% over five years to a 95% level of confidence. A significant net decrease in UTC (−2.4%) was only detected in one of the six suburbs. Two suburbs had a net increase in UTC by +2.7% over five years. On private land, there was often areas of UTC loss, but this was generally offset by canopy gain in other areas of the private realm as well as in streetscapes and public land. Losses in UTC on private land were mainly due to tree removal, with or without subsequent construction works.This study describes a simple, but statistically rigorous, method to quantify UTC change and the drivers of change in different land-use types and tenure. Despite studying two suburbs will high rates of infill development, only one suburb showed evidence of net UTC decrease. The ‘dynamic equilibrium’ in UTC, whereby canopy losses area approximately offset by concurrent canopy gain, means that ambitious targets being set by local governments to increase UTC may be difficult to achieve without changes in tree protection and infill development policy and planning. 相似文献
3.
Inter-annual canopy growth is one of the key indicators for assessing forest conditions, but the measurements require laborious field surveys. Up-to-date LiDAR remote sensing provides sufficient three-dimensional morphological information of the ground to monitor canopy heights on a broad scale. Thus, we attempted to use multi-temporal airborne LiDAR datasets in the estimation of vertical canopy growth, across various types of broad-leaved trees in a large urban park.The growth of broad-leaved canopies in the EXPO '70 urban forest in Osaka, Japan was assessed with 19 plots at the stand level and 39 selected trees at the individual-tree level. Airborne LiDAR campaigns repeatedly observed the park in the summers of 2004, 2008, and 2010. We acquired canopy height models (CHMs) for each year from the height values of the uppermost laser returns at every 0.5 m grid. The annual canopy growth was calculated by the differences in CHMs and validated with the annual changes in field-measured basal areas and tree heights.LiDAR estimations revealed that the average annual canopy growth from 2004 to 2010 was 0.26 ± 0.11 m m−2 yr−1 at the plot level and 0.26 ± 0.10 m m−2 yr−1 at the individual-tree level. This result showed that growing trends were consistent at different scales through 2004 to 2010 despite uncertainty in estimating short-term growth for small crown areas at the individual-tree level. This LiDAR-estimated canopy growth shows a moderate relation to field-measured increase of basal areas and average heights. The estimation uncertainties seem to result from the complex canopy structure and irregular crown shape of broad-leaved trees. Challenges still remain on how to incorporate the growth of understory trees, growth in the lateral direction, and gap dynamics inside the canopy, particularly in applying multi-temporal LiDAR datasets to the large-scale growth assessment. 相似文献
4.
Urban forest is a crucial part of urban ecological environment. The accurate estimation of its tree aboveground biomass (AGB) is of significant value to evaluate urban ecological functions and estimate urban forest carbon storage. It has a high accuracy to estimate the forest AGB with field measured canopy structure parameters, but unsuitable for large-scale operations. Limited by low spatial resolution or spectral saturation, the estimated forest AGBs based on various satellite remotely sensed data have relatively low accuracies. In contrast, Unmanned Aerial Vehicle (UAV) remote sensing provides a promising way to accurately estimate the tree AGB of fragmented urban forest. In this study, taking an artificial urban forest in Ma'anxi Wetland Park in Chongqing City, China as an example, we used UAVs equipped with a digital camera and a LiDAR to acquire two point cloud data. One was produced from overlapping images using Structure from Motion (SfM) photogrammetry, and the other was resolved from laser scanned raw data. The dual point clouds were combined to extract individual tree height (H) and canopy radius (Rc), which were then input to the newly established allometric equation with tree H and Rc as predictor variables to obtain the AGBs of all dawn redwood trees in study area. In accuracy assessment, the coefficient of determination (R2) and Root Mean Square Error (RMSE) of extracted H were 0.9341 and 0.59 m; the R2 and RMSE of extracted Rc were 0.9006 and 0.28 m; the R2 and RMSE of estimated AGB were 0.9452 and 17.59 kg. These results proved the feasibility and effectiveness of applying dual-source UAV point cloud data and the new allometric equation on H and Rc to accurate AGB estimation of urban forest trees. 相似文献
5.
Birds are ecosystem service providers and excellent urban ecosystem indicators because they are sensitive to habitat structure. Light detection and ranging (LiDAR) technology is a promising tool in bird habitat characterization because it can directly acquire fine-scale 3-D information over large areas; however, most of past avian ecological studies using LiDAR were conducted in North America and Europe, and there have been no studies in Asia. The robustness of LiDAR data across different habitat types remain problematic. In this study, we set 13 plots having different canopy area percentages in a large-scale urban park in Japan, and examined the usefulness of airborne LiDAR data in modeling richness and diversity of forest bird species and the abundance of Paridae species that play an important role in the urban food web. Bird surveys were conducted eight times at each plot during the birds’ breeding season, and the results were estimated using generalized linear models. In consequence, all of the response variables were explained by one or a few LiDAR variables, and the 1 × 1 × 1-m voxel-based variables were especially robust estimators. When targeting only densely-forested plots having more than 60% canopy area, the LiDAR data efficiency declined in estimation of the richness and diversity of whole forest bird species, whereas a laser penetration rate was efficient for estimating the Paridae species abundance. These results implied that the LiDAR data are useful in habitat characterization of forest birds, and even when targeting only dense forests, some LiDAR variables are effective for habitat estimation of birds preferring specific forest structures. In the future, application of LiDAR across a variety of ecosystems will greatly serve to develop adaptive conservation and management planning for urban forests. 相似文献
6.
Accurately mapping carbon stocks of urban trees is necessary for urban managers to design strategies to mitigate climate change. However, the aboveground carbon stocks of urban trees are usually underestimated by passive remote sensing data because of the signal saturation problem. The research is the first attempt to develop a framework to map aboveground carbon density of trees in urban areas by synergizing Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data with Gaofen-2 (GF-2) imagery. The framework consists of three key steps. First, we used a support vector machine classifier to classify GF-2 images and extracted urban tree regions. Second, we estimated the tree carbon density of ICESat-2 strips by developing a ICESat-2 photon feature-based aboveground carbon density estimation model. Third, we mapped the carbon density of urban trees by developing a synergistic model between ICESat-2 and GF-2 data based on an object-oriented method. We tested the approach for the areas within the fifth ring road of Beijing, China. The results showed that the 50th percentile height (PH50) of nighttime photons was a good predictor for estimating carbon density of urban trees, with a R2 of 0.69 and a Root Mean Square Error (RMSE) of 2.81 kg C m−2. Using the spectral features generated by GF-2 imagery, we could further extrapolate the carbon density estimated by ICESat-2 strip data to a full coverage of accurate mapping carbon density by urban trees, resulting in a R2 of 0.64 and a RMSE of 2.32 kg C m−2. The carbon stocks within the fifth ring road of Beijing were 8.28 × 108 kg in total, with the mean carbon density of 3.52 kg C m−2. Such estimations were larger than that of previous study using passive remote sensing data only, suggesting the integration of spaceborne LiDAR and spectral data could greatly reduce the underestimation of carbon stocks of urban trees. Our approach can more accurately estimate carbon stocks of urban trees and has the potential to be applicable in other cities. 相似文献
7.
Trees provide important health, ecosystem, and aesthetic services in urban areas, but they are unevenly distributed. Some neighborhoods have abundant tree canopy and others nearly none. We analyzed how neighborhood characteristics and changes in income over time related to the distribution of urban tree canopy in Washington, D.C. and Baltimore, MD. We used stepwise multiple regression analysis to identify strong predictors of UTC, from variables found in neighborhoods with different patterns of wealth-stability over time. We then built spatial lag models to predict variation in UTC cover, using the results of a Principal Component Analysis of the socioeconomic, demographic, and housing characteristics of the two cities. We found that: (1) stable-wealthy neighborhoods were more likely to have more, and more consistent, tree canopy cover than other neighborhood types; (2) decreases and increases in income were negatively associated with UTC in Washington, D.C. but not Baltimore, where income stability in both wealthy and impoverished neighborhoods was a significant predictor of UTC; and (3) the association of high socioeconomic status with UTC coverage varied between the two cities. 相似文献
8.
We applied drone remote sensing to identify relationships between key forest health indicators collected in the field and four Vegetative Indices (VI) to improve conservation management of urban forests. Key indicators of urban forest health revealed several areas of conservation concern including a majority of overstory trees in moderate to severe decline, canopy gaps, anthropogenic dumping, vines overtaking the forest canopy, and invasion by non-native plant species. We found plot-level vegetation index (VI) values of NDVI, NDRE, GNDVI, and GRVI calculated from drone imagery are significantly related to the impact of several of these ecological concerns as well as metrics of forest composition and equitability. Despite the small number of plots, too few to provide a general predictive framework, these findings indicate a substantial potential for drone remote sensing as a low-cost, efficient tool for urban forest management. We discuss how our findings can advance urban forest management and discuss challenges and opportunities for future drone VI research in urban natural areas. 相似文献
9.
Urban tree species identification is the basis for studying the urban-environment coordination mechanism at the species level. Although the gradual maturity of remote sensing data and related research including light detection and ranging (LiDAR) provides a good foundation for the realization of this technology, multiple reasons such as cost, data openness, study scope limitations, and weakness of traditional morphological features make such data still challenging to apply to subtropical urban trees with heterogeneous canopy structures and high biodiversity. To address the problem, we developed two large-scale LiDAR morphological features in this research by, 1) modifying the rotate image method based on the axisymmetric structure to make it easier to use, and 2) developing an innovative adaptive ellipsoid method to extract the canopy features of the non-axisymmetric structure effectively. We evaluated the ability of these two morphological features to describe 12 common subtropical urban tree (SUT) species in Hong Kong growing in urban parks and streets, obtaining an accuracy of 88%. And the advantages of the proposed method are demonstrated by comparison with existing LiDAR morphological features and mean decrease accuracy (MDA) analysis. Our results illustrated that the rotate image feature based on the axisymmetric structure did not perform as well as the adaptive ellipsoid feature based on the non-axisymmetric structure in SUT, and the combined application of these two new morphological features got further accuracy improvement. The method proposed in this study had significant advantages in terms of accuracy, the number of species included, and generalisation capability compared to existing studies on the identification of subtropical urban trees. 相似文献
10.
Ecosystem service estimation is a very popular topic. Many urban studies use the i-Tree Eco model developed by US Forest Service to estimate ecosystem services. Several ecosystem service estimation studies have been conducted acting upon the assumption that relationships developed elsewhere are applicable to sites that vary in species, site, climate, and environmental conditions. This study tested the accuracy of highly used existing leaf area and biomass models when used outside the region in which it was developed. To do this, we measured 74 urban trees from five species in Stevens Point, Wisconsin collecting data such as diameter at breast height (Dbh), tree height, height to the base of live crown, crown width, crown volume, leaf area, and leaf dry weight biomass. Using the data, we developed two models each to predict leaf area and biomass. Using ten independent samples, we compared our predictions with predictions from the existing models which are also used in i-Tree. Our results indicated that the local models developed in the current study predicted leaf area and biomass better than existing models which had higher prediction error. The difference in prediction will ultimately affect ecosystem services estimation when. using i-Tree, and future studies should acknowledge the difference. 相似文献
11.
Urban trees store and sequester large amounts of carbon and are a vital component of natural climate solutions. Despite the well-recognized carbon benefits of urban trees, there is limited effort to examine how spatial distribution of carbon density varies across distinctive social, demographic, and built dimensions of urban landscapes. Moreover, it is unclear whether specific aspects of landscape structure and design could help increase carbon densities in urban trees. Here, we produced a fine-resolution carbon density map of urban trees in New York City (NYC) by integrating high-resolution land cover map, LiDAR-derived tree metrics, i-Tree Eco, and field survey data. We then explored spatial variations of carbon density across the gradients of urban development intensity, social deprivation index, and neighborhood age, and we examined the relationships between carbon density, and fragmentation, aggregation, size, and shape of tree canopy cover. We find that carbon stored in urban trees in NYC is estimated as 1078 Gg, with an average density of 13.8 Mg/ha. This large amount of carbon is unevenly distributed, with carbon densities being highest in Bronx and in open parks and street trees. Furthermore, carbon densities are negatively associated with urban development intensity and the social gradient of deprivation. Regarding the impacts of tree morphology on carbon density, our results show that while the amount of tree cover is the most influential factor in determining carbon density, small-sized forest patches and moderate levels of forest edges are also conductive to increasing carbon densities of urban trees. To incorporate urban forestry into developing innovative, effective, and equitable climate mitigation strategies, planners and decision makers need to identify the optimal spatial configuration of urban forests and invest in tree planting programs in marginalized communities. 相似文献
12.
Context
Digital elevation models (DEMs) are widely used in landscape ecology to link topographic features with biotic and abiotic factors. However, to date, high-resolution, affordable, and easy to process elevation data are not available for many regions.Objectives
Here we propose a field-based method for efficiently and inexpensively collecting or analysing already existing slope data. We compare the field approach to two commonly used remote sensing techniques to test the similarly of the DEMs using different methods.Methods
To provide an ecological example of the method, we selected four 1-ha forest plots and compared the DEM generated by using our field method with those derived from: (i) coarse (~ 30 m pixel) data from the Shuttle Radar Topography Mission and (ii) high-resolution (~ 1 m) data from Light Detection and Ranging devices (LiDAR).Results
Field- and LiDAR-based DEMs showed strong concordance in two of the four sites. The sites where field-based and LiDAR DEMs substantially differed, suffered from relatively few LiDAR sampling points. Diagnostic tests suggested that the field–LiDAR discrepancy was due to dense over-storey vegetation, which reduced LiDAR’s accuracy due to a failure to penetrate the forest canopy adequately in some areas.Conclusions
Our method has the advantage of being quick and cheap to collect yet able to produce small-scale (plot-scale) DEMs of high quality. By using the R-code we have provided, ecologists will be able to use slope data (collected using any means) to generate a DEM without the need of specific skills in spatial sciences.13.
Urban forests are recognized as a nature-based solution for stormwater management. This study assessed the underlying processes and extent of runoff reduction due to street trees with a paired-catchment experiment conducted in two sewersheds of Fond du Lac, Wisconsin. Computer models are flexible, fast, and low-cost options to generalize and assess the hydrologic processes determined in field studies. A state-of-the-art, public-domain model, which explicitly simulates urban tree hydrology, i-Tree Hydro, was used to simulate the paired-catchment experiment, and results from field observations and simulation predictions were compared to assess model validity and suitability as per conditions in the broader Great Lakes basin. Model parameters were aligned with observed conditions using automatic and manual calibration. Model performance metrics were used to quantify the weekly performance of calibration and to validate predictions. Those calibration metrics differed substantially between the two periods simulated, but most calibration metrics remained positive, indicating the model was not fitting only the period used for calibration. Predicted avoided runoff for a five-month leaf-on period was 64 L/m2 of canopy, 4 % lower than the field-estimated avoided runoff of 66 L/m2 of canopy. Interception was the most directly comparable process between the model and field observations. Based on 5 storms sampled, field estimation of precipitation intercepted and retained on trees averaged 63 % and ranged from 22 % to 81 %, while model estimation averaged 61 % and ranged from 36 % to 99 %. This model was able to fit predictions to observed catchment discharge but required extensive manual calibration to do so. The i-Tree Hydro model predicted avoided runoff comparable with the field study and earlier assessments. Additional field studies in similar settings are needed to confirm findings and improve transferability to other tree species and environmental settings. 相似文献
14.
Trees are an integral component of the urban environment and important for human well-being, adaption measures to climate change and sustainable urban transformation. Understanding the small-scale impacts of urban trees and strategically managing the ecosystem services they provide requires high-resolution information on urban forest structure, which is still scarce. In contrast, there is an abundance of data portraying urban areas and an associated trend towards smart cities and digital twins as analysis platforms. A GIS workflow is presented in this paper that may close this data gap by classifying the urban forest from LiDAR point clouds, detecting and reconstructing individual crowns, and enabling a tree representation within semantic 3D city models. The workflow is designed to provide robust results for point clouds with a density of at least 4 pts/m2 that are widely available. Evaluation was conducted by mapping the urban forest of Dresden (Germany) using a point cloud with 4 pts/m². An object-based data fusion approach is implemented for the classification of the urban forest. A classification accuracy of 95 % for different urban settings is achieved by combining LiDAR with multispectral imagery and a 3D building model. Individual trees are detected by local maxima filtering and crowns are segmented using marker-controlled watershed segmentation. Evaluation highlights the influences of both urban and forest structure on individual tree detection. Substantial differences in detection accuracies are evident between trees along streets (72 %) and structurally more complex tree stands in green areas (31 %), as well as dependencies on tree height and crown diameter. Furthermore, an approach for parameterized reconstruction of tree crowns is presented, which enables efficient and realistic city-wide modeling. The suitability of LiDAR to measure individual tree metrics is illustrated as well as a framework for modeling individual tree crowns via geometric primitives. 相似文献
15.
Spatial information on urban forest canopy height (FCH) is fundamental for urban forest monitoring and assisting urban planning and management. Traditionally, ground-based canopy height measurements are time-consuming and laborious, making it challenging for periodic inventory of urban FCH at crown level. Airborne-light detection and ranging (LiDAR) sensor can efficiently measure crown-level FCH; however, the high cost of airborne-LiDAR data collection over large scales hinders its wide applications at a high temporal resolution. Previous studies have shown that in some cases, the Unmanned Aerial Vehicle (UAV)-digital aerial photogrammetry (DAP) approach (i.e., UAV-based structure from motion algorithm) is equivalent to or even outperform airborne-LiDAR in measuring forest structure, but few studies have evaluated their performances in measuring FCH in more complex urban environment, across non-ground coverage (including both canopy and building coverage) and topographical slope gradients. Also, the contribution of multi-angle measurement technique from UAV-DAP to FCH estimation accuracy has rarely been explored in the urban environment. Here, we compared the performances of UAV-LiDAR and UAV-DAP approaches on measuring thousands of crown-level FCH at different non-ground coverage and topographical slope areas in an urban environment. Specifically, UAV-LiDAR-based spatial measurements of crown-level FCH were used as the reference after ground-based validation (R2 = 0.88, RMSE = 2.36 m). The accuracy of UAV-DAP approach with/without multi-angle measurement in different non-ground coverage and topographical slope areas were then analyzed. The results showed that although the DAP multi-angle-based approach can improve the accuracy of spatial measurement for crown-level FCH in some cases, non-ground coverage (including both canopy and building coverage) was still the main factor affecting the broad applications of DAP approach in measuring urban FCH: at areas where non-ground coverage < 0.95, no matter how topographical slope varied, the accuracy of DAP approach was high (R2 = 0.86∼0.94, RMSE = 1.56∼2.93 m); at areas where non-ground coverage > 0.95, except for the case of flat areas (i.e., topographical slope <= 2°), the accuracy of DAP was poor (R2 = 0.20, RMSE = 12.34 m). However, using LiDAR-digital terrain model (DTM) instead of DAP-DTM, at areas where non-ground coverage > 0.95, can significantly improve the accuracy of UAV-DAP approach in measuring crown-level FCH (R2 = 0.91, RMSE =1.61 m). Our study thus provides a complete guidance on the usage of cost-effective UAV-DAP approach for measuring crown-level FCH in the urban environment, which will be helpful for precise urban forest management and improving the efficiency of urban environmental planning. 相似文献
16.
Leaf area of urban vegetation is an important ecological characteristic, influencing urban climate through shading and transpiration cooling and air quality through air pollutant deposition. Accurate estimates of leaf area over large areas are fundamental to model such processes. The aim of this study was to explore if an aerial LiDAR dataset acquired to create a high resolution digital terrain model could be used to map effective leaf area index (Le) and to assess the Le variation in a high latitude urban area, here represented by the city of Gothenburg, Sweden. Le was estimated from LiDAR data using a Beer-Lambert law based approach and compared to ground-based measurements with hemispherical photography and the Plant Canopy Analyser LAI-2200. Even though the LiDAR dataset was not optimized for Le mapping, the comparison with hemispherical photography showed good agreement (r2 = 0.72, RMSE = 0.97) for urban parks and woodlands. Leaf area density of single trees, estimated from LiDAR and LAI-2200, did not show as good agreement (r2 = 0.53, RMSE = 0.49). Le in 10 m resolution covering most of Gothenburg municipality ranged from 0 to 14 (0.3% of the values >7) with an average Le of 3.5 in deciduous forests and 1.2 in urban built-up areas. When Le was averaged over larger scales there was a high correlation with canopy cover (r2 = 0.97 in 1 × 1 km2 scale) implying that at this scale Le is rather homogenous. However, when Le was averaged only over the vegetated parts, differences in Le became clear. Detailed study of Le in seven urban green areas with different amount and type of greenery showed a large variation in Le, ranging from average Le of 0.9 in a residential area to 4.1 in an urban woodland. The use of LiDAR data has the potential to considerably increase information of forest structure in the urban environment. 相似文献
17.
Jon Pasher Mark McGovern Michael Khoury Jason Duffe 《Urban Forestry & Urban Greening》2014,13(3):484-494
Urban trees are important components of the landscape and offer numerous benefits; both socio-economical and biophysical. Urban trees act as a sink for CO2, helping to offset carbon emissions from urban areas by removing the greenhouse gas from the atmosphere through photosynthesis. Environment Canada develops estimates of Canada's greenhouse gas emissions and removals which are submitted annually to the United Nations as part of ongoing commitments under the United Nations Framework Convention for Climate Change. As part of these reporting commitments countries are required to develop estimates of emissions and removals of Greenhouse Gas that are the result of direct impact of human activities in the Land-Use, Land-Use Change and Forestry Sector. Here, we present an approach which involves sampling high resolution aerial photographs to determine urban tree coverage across Canada's major urban areas. Our results suggest Canadian urban areas have an estimated tree canopy cover of 27%. This tree cover is estimated to store approximately 34,000 kt C and annually sequester approximately 2500 kt of CO2. These estimates show significant improvement over previous methods used to provide Canadian estimates. The methods developed here are easily repeatable which allow for temporal changes to be analyzed and assessed over time. 相似文献
18.
Stormwater Green Infrastructure (SGI) systems such as rain gardens, permeable pavement and bioswales are commonly used in municipalities to reduce urban flooding and water pollution. In conjunction with these direct benefits, SGI systems provide additional social and environmental “co-benefits”. Our goal was to investigate the co-benefits of commonly used SGI systems in five cities in the United States, including Baltimore, Denver, New York City, Philadelphia, and Portland. The i-Tree Eco model was used to predict carbon storage and sequestration, air pollution removal, UV reduction, and cooling effects of trees for individual tree species and estimated SGI tree inventories across the five study cities based on observed tree characteristic data. Aspects of SGI design, environmental factors, and model inputs were assessed to understand what parameters impacted SGI co-benefits predicted by the model. We evaluated the most highly influential parameters using a global sensitivity analysis method. As expected, the type of SGI design, and the overall number of trees utilized within those designs, played a large role in determining the overall amount of co-benefits predicted by the model. However, climate also influenced estimation of benefits produced, with similar responses predicted for cities in the same climate zone (e.g. Baltimore, Philadelphia, and New York City). In particular, the global sensitivity analysis showed that variables influencing environmental conditions and tree growth also impacted final co-benefit predictions produced by i-Tree Eco. study revealed how various assumptions and prevailing equations within the i-Tree Eco model can play a major role in the final outcomes predicted by the model. Studies that use i-Tree Eco to analyze potential co-benefits of SGI projects, especially when the goal is to compare projects across climate zones, should consider what aspects of the results are simply a function of the model itself. Overall, the model predicts that more co-benefits are provided in certain climate zones, an assumption currently supported in the literature. 相似文献
19.
利用冠层分析仪测算苹果园叶面积指数及其可靠性分析 总被引：7，自引：0，他引：7
以山东省苹果主产区成龄密植园、间伐园和疏枝园苹果树为材料,利用方框取样法（直接法,di）和冠层分析仪法（间接法,in）分别测定了叶面积指数（LAI）。结果表明：冠层分析仪法与方框取样法测量的LAI 平均值分别为3.0±0.2 和4.3±0.2,前者比后者平均低30.2%,且随着LAI 增加,二者相差增大.密植园、间伐园和疏枝园相应偏低32.0%、35.5%和25.0%。统计分析表明,LAIdi 与LAIin 相关性极显著,密植园、间伐园和疏枝园中的相关系数（r）分别为0.91、0.89 和0.92。把冠层分析仪鱼眼摄像头最外圈去掉（平均视天顶角为68º）LAIdi 与LAIin 之间相差在13%以内,校正后密植园、间伐园和疏枝园中二者的相关系数（r）分别为0.93、0.93 和0.94。尽管方框取样法和冠层分析仪法测量的LAI 具有极显著的相关性,但冠层分析仪法的测值偏低,因此,在应用冠层分析仪估计苹果园的LAI 时,要改进冠层分析仪的测量方法,并利用方框取样法测得的结果对其进行校正,以便提高准确性。在本研究中,密植园、间伐园和疏枝园内冠层分析仪的校正系数分别为1.3004、1.2077 和1.1762。 相似文献
20.
Individual Tree Inventory (ITI) is critical for urban planning, including urban heat mitigation. However, an ITI is usually incomplete and costly due to data collection challenges in the dynamic urban landscape. This research developed a methodical GeoAI framework to build a comprehensive ITI and quantify tree species cooling on rising urban heat.The object detection Faster R-CNN model with Inception ResNet V2 was implemented to detect individual trees canopy and seven tree species (Callery pear, Chinese elm, English elm, Mugga ironbark, Plane tree, Spotted gum and White cedar). The land surface temperature (LST) was derived from Landsat 8 surface reflectance imagery. Two models for ITI were further developed for spatial and statistical analysis. Firstly, an ‘Individual tree-based model’ stores the attributes of tree species and its vertical configuration obtained from LiDAR, along with its tree canopy area and surface temperature. Secondly, the ‘LST zone-based model’ stores tree canopy cover and building areas in each zone unit. Pearson correlation, global linear regression, and geographically weighted regression (GWR) were applied to establish the relationship between tree attributes, building areas (explanatory variables) with local temperature (dependent variable). Results showed that English elm has the highest cooling and least by Mugga ironbark in the study area. GWR results demonstrate that 94% of the LST was explained by tree height and tree canopy area. The LST zone-based model showed that 85% of the LST was explained by the percentage of tree species and buildings. Maps of the local R2 and coefficients of the independent variables provide spatially explicit information on the cooling of different tree species compared to building areas. The implemented GeoAI approach provides important insights to urban planners and government to monitor urban trees with the enhanced Individual Tree Inventory and strategies mitigation plan to reduce the impact of climate change and global warming. 相似文献