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Ms. Red Willow Coleman
harvey mudd college

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Research Keywords & Expertise

0 Computational Biology
0 Remote Sensing Applications
0 Urban and rural ecosystems
0 Data Science/Machine Learning
0 Machine Learning Applications to Earth Sciences

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Journal article
Published: 15 December 2020 in Remote Sensing
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It is important to understand the distribution of irrigated and non-irrigated vegetation in rapidly expanding urban areas that are experiencing climate-induced changes in water availability, such as Los Angeles, California. Mapping irrigated vegetation in Los Angeles is necessary for developing sustainable water use practices and accurately accounting for the megacity’s carbon exchange and water balance changes. However, pre-existing maps of irrigated vegetation are largely limited to agricultural regions and are too coarse to resolve heterogeneous urban landscapes. Previous research suggests that irrigation has a strong cooling effect on vegetation, especially in semi-arid environments. The July 2018 launch of the ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) offers an opportunity to test this hypothesis using retrieved land surface temperature (LST) data in complex, heterogeneous urban/non-urban environments. In this study, we leverage Landsat 8 optical imagery and 30 m sharpened afternoon summertime ECOSTRESS LST, then apply very high-resolution (0.6–10 m) vegetation fraction weighting to produce a map of irrigated and non-irrigated vegetation in Los Angeles. This classification was compared to other classifications using different combinations of sensors in order to offer a preliminary accuracy and uncertainty assessment. This approach verifies that ECOSTRESS LST data provides an accurate map (98.2% accuracy) of irrigated urban vegetation in southern California that has the potential to reduce uncertainties in regional carbon and hydrological cycle models.

ACS Style

Red Coleman; Natasha Stavros; Glynn Hulley; Nicholas Parazoo. Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California. Remote Sensing 2020, 12, 4102 .

AMA Style

Red Coleman, Natasha Stavros, Glynn Hulley, Nicholas Parazoo. Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California. Remote Sensing. 2020; 12 (24):4102.

Chicago/Turabian Style

Red Coleman; Natasha Stavros; Glynn Hulley; Nicholas Parazoo. 2020. "Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California." Remote Sensing 12, no. 24: 4102.

Journal article
Published: 26 July 2020 in Remote Sensing
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High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of urban vegetation cover in the Southern California Air Basin (SoCAB) with publicly available satellite imagery. This method uses Sentinel-2 (10–60 × 10–60 m) and National Agriculture Imagery Program (NAIP) (0.6 × 0.6 m) optical imagery to classify urban and non-urban areas of impervious surface, tree, grass, shrub, bare soil/non-photosynthetic vegetation, and water. Our approach was designed for Los Angeles, a geographically complex megacity characterized by diverse Mediterranean land cover and a mix of high-rise buildings and topographic features that produce strong shadow effects. We show that a combined NAIP and Sentinel-2 classification reduces misclassified shadow pixels and resolves spatially heterogeneous vegetation gradients across urban and non-urban regions in SoCAB at 0.6–10 m resolution with 85% overall accuracy and 88% weighted overall accuracy. Results from this study will enable the long-term monitoring of land cover change associated with urbanization and quantification of biospheric contributions to carbon and water cycling in cities.

ACS Style

Red Coleman; Natasha Stavros; Vineet Yadav; Nicholas Parazoo. A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity. Remote Sensing 2020, 12, 2399 .

AMA Style

Red Coleman, Natasha Stavros, Vineet Yadav, Nicholas Parazoo. A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity. Remote Sensing. 2020; 12 (15):2399.

Chicago/Turabian Style

Red Coleman; Natasha Stavros; Vineet Yadav; Nicholas Parazoo. 2020. "A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity." Remote Sensing 12, no. 15: 2399.