A statewide urban tree canopy mapping method

Erker, Tedward; Wang, Lei; Lorentz, Laura J.; Stoltman, Andrew M.; Townsend, Philip A.

doi:10.1016/j.rse.2019.03.037

Cited by 32 publications

(37 citation statements)

References 32 publications

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“…For tree cover we used a 1 m resolution landcover map derived from 2013 National Agriculture Inventory Program (NAIP) visible and near-infrared digital aerial imagery with an accuracy of 85% (Erker et al 2018). Using building footprints from the Dane county, for each house for which we had energy use data, we divided the space around it into 8 regions defined by 2 buffers around the house of distance 20 m and 60 m and 4 rays from the building's centroid.…”

Section: Tree Canopymentioning

confidence: 99%

Trees in cool climate cities may increase atmospheric carbon by altering building energy use

Erker

Townsend

2019

Environ. Res. Commun.

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Urban trees are a critical part of the 'green infrastructure' intended to make our growing cities more sustainable in an era of climate change. The potential for urban trees to modify microclimates and thereby reduce building energy use and the associated carbon emissions is a commonly cited ecosystem service used to justify million tree planting campaigns across the US. However, what we know of this ecosystem service comes primarily from unvalidated simulation studies. Using the first dataset of actual heating and cooling energy use combined with tree cover data, we show that contrary to the predictions of the most commonly used simulations, trees in a cool climate city increase carbon emissions from residential building energy use. This is driven primarily by near east (<20 m from building) tree cover. Further analysis of urban areas in the US shows that this is likely the case in cool climates throughout the country, encompassing approximately 39% of the US population and 62% of its area (56%, excluding Alaska). This work adds geographic nuance to our understanding of how urban shade trees affect the carbon budget, and it could have major implications for tree planting programs in cool climates.

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Section: Tree Canopymentioning

confidence: 99%

Trees in cool climate cities may increase atmospheric carbon by altering building energy use

Erker

Townsend

2019

Environ. Res. Commun.

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“…Conducting object-based image analysis (OBIA) with high-resolution RS data has become a common practice for vegetation mapping [11][12][13][14]. OBIA considers a patch of vegetation as a unit rather than a pixel, thus it is less impacted by noise caused by within-class variation associated with high spatial resolution than pixel-based image analysis (PBIA) [11].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, PBIA mainly uses features from the spectral domain, whereas OBIA can use features from both the spectral and spatial domains. Thus, OBIA can integrate spectral, geometric, and textural features leading to good accuracy for vegetation mapping [11][12][13][14][15]. With increasing availability, lidar (light detection and ranging) data have been integrated with multispectral RS data and OBIA to provide structural features of vegetation to improve classification accuracy [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

et al. 2020

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Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

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“…Conducting object-based image analysis (OBIA) with high-resolution RS data has become a common practice for vegetation mapping [11][12][13][14]. OBIA takes a patch of vegetation as a unit rather than a pixel, thus is less impacted by noise caused by within-class variation caused by high spatial resolution than pixel-based image analysis (PBIA) [11].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, different from PBIA mainly using features from the spectral domain, OBIA can use features from both spectral and spatial domains. Thus, OBIA can integrate spectral, geometric, and textural features leading to good accuracy for vegetation mapping [11][12][13][14][15]. With increasing availability, lidar (light detection and ranging) data have been integrated with multispectral RS data and OBIA method to provide structural features of vegetation to improve classification accuracy [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data over Large Spatial Area: A Case Study with Kudzu

Liang¹,

Abidi²,

Carrasco³

et al. 2019

Preprint

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Mapping vegetation species is critical to facilitate related quantitative assessment, and for invasive plants mapping their distribution is important to enhance monitoring and controlling activities. Integrating high resolution multispectral remote sensing (RS) image and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple source of high-resolution RS data for vegetation mapping at large spatial scale can be both computationally and sampling intensive. Here we designed a two-step classification workflow to decrease computational cost and sampling effort, and to increase classification accuracy by integrating multispectral and lidar data to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1,362 km2) of Tennessee, the United States. Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the Producer’s Accuracy, User’s Accuracy, and Kappa for the SVM model on kudzu was 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions or other vegetation species.

show abstract

A statewide urban tree canopy mapping method

Cited by 32 publications

References 32 publications

Trees in cool climate cities may increase atmospheric carbon by altering building energy use

Trees in cool climate cities may increase atmospheric carbon by altering building energy use

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data over Large Spatial Area: A Case Study with Kudzu

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