Drought response of urban trees and turfgrass using airborne imaging spectroscopy

Miller, Drew; Alonzo, Michael; Roberts, Dar A.; Tague, Christina; McFadden, J. P.

doi:10.1016/j.rse.2020.111646

Cited by 42 publications

(34 citation statements)

References 81 publications

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“…First derivative reflectance (FDR) analysis is one of the common spectral pre-processing techniques, and it effectively removes background effects and enhances subtle spectral features as well as enhancing weak spectral features [45,46]. The continuum-removal (CR) transformation, which is a brightness normalization technique that fits a convex hull over the original reflectance data, has also been applied to enhance the spectral features and eliminate or reduce unrelated effects [47,48]. Standard normal variate (SNV) and multiplicative scatter correction (MSC) could be used to reduce the noise in the raw reflectance data caused by light scattering and baseline drift [49].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

et al. 2020

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Japanese horseradish (wasabi) grows in very specific conditions, and recent environmental climate changes have damaged wasabi production. In addition, the optimal culture methods are not well known, and it is becoming increasingly difficult for incipient farmers to cultivate it. Chlorophyll a, b and carotenoid contents, as well as their allocation, could be an adequate indicator in evaluating its production and environmental stress; thus, developing an in situ method to monitor photosynthetic pigments based on reflectance could be useful for agricultural management. Besides original reflectance (OR), five pre-processing techniques, namely, first derivative reflectance (FDR), continuum-removed (CR), de-trending (DT), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV), were compared to assess the accuracy of the estimation. Furthermore, five machine learning algorithms—random forest (RF), support vector machine (SVM), kernel-based extreme learning machine (KELM), Cubist, and Stochastic Gradient Boosting (SGB)—were considered. To classify the samples under different pH or sulphur ion concentration conditions, the end of the red edge bands was effective for OR, FDR, DT, MSC, and SNV, while a green-peak band was effective for CR. Overall, KELM and Cubist showed high performance and incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy. The best combinations were found to be DT–KELM for chl a (RPD = 1.511–5.17, RMSE = 1.23–3.62 μg cm−2) and chl a:b (RPD = 0.73–3.17, RMSE = 0.13–0.60); CR–KELM for chl b (RPD = 1.92–5.06, RMSE = 0.41–1.03 μg cm−2) and chl a:car (RPD = 1.31–3.23, RMSE = 0.26–0.50); SNV–Cubist for car (RPD = 1.63–3.32, RMSE = 0.31–1.89 μg cm−2); and DT–Cubist for chl:car (RPD = 1.53–3.96, RMSE = 0.27–0.74).

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Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

et al. 2020

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“…(2012) identify a 1.4% increase in water consumption for every 1°F increase in nighttime temperature in Phoenix, Arizona, highlighting the importance of feedbacks between the built environment and urban vegetation. However, recent studies have shown that during drought, urban irrigation and greenness can become decoupled, and increases in irrigation do not result in maintained vegetation greenness through the duration of a drought for any vegetation type (Miller et al., 2020; Quesnel et al., 2019). Because vegetation responds most strongly to climate signals, increased outdoor water use in a hotter climate is unlikely to maintain verdant, lush vegetation, prompting continued overapplication of urban irrigation or adaptation of urban vegetation regimes.…”

Section: Discussionmentioning

confidence: 99%

Satellites to Sprinklers: Assessing the Role of Climate and Land Cover Change on Patterns of Urban Outdoor Water Use

Blount

Wolfand

Bell³

et al. 2021

Water Resources Research

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Outdoor water use represents over 50% of total water demand in semiarid and arid cities and presents both challenges to and opportunities for improved efficiency and water resilience. The current work adapts a remote sensing‐based methodology to estimate growing season irrigation rates at the census block group scale in Denver, Colorado. Results show that city‐wide outdoor water use does not change significantly from 1995 to 2018, while per capita water use and total water use significantly decrease from 2000 to 2018. Because total water use, but not outdoor use, is decreasing, the percent of water used outdoors significantly increases across the city from 2000 to 2018. Climate variables account for one‐quarter of interannual variation in mean irrigation rates due primarily to changes in temperature, not precipitation. Percent impervious land cover exhibits a significant inverse nonlinear relationship with irrigation rates at the census block group scale. Finally, 38% of Denver census block groups show significantly increasing irrigation rates between 1995 and 2018 driven primarily by increasing temperatures. The increasing proportion of water used for irrigation highlights the importance of outdoor demand management for urban water systems as indoor efficiencies improve. We advocate that resilient water systems necessitate integrated land use, infrastructure, and water planning in the face of urban growth and climate change. While minimizing irrigated urban areas may reduce demand, remaining green spaces should be designed to maximize multiple benefits including reductions in water demand and urban heat islands, stormwater management, and recreation to improve the sustainability of growing cities.

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“…The preliminary analysis selected a set of candidate factors, including the climatic variables (precipitation and surface temperature), terrain (DEM and slope), transportation impact (density of railways, roads, and waterways), and urbanization level (in the proxy of the normalized difference built-up index or NDBI). Those factors have proved to be related to the changes of the urban greenness landscape by many other studies [3,20]. The combination of those economic and environmental factors may provide a more comprehensive view for understanding the driving forces for the urban greenness space.…”

Section: Factor Analysis For the Dynamics Of The Urban Greennessmentioning

confidence: 92%

“…First, vegetation greenness can be strongly affected by climate changes. In arid regions, water shortage placed a critical constraint for both natural and urban vegetation [20]. Second, the urban greenness space could be significantly altered by human activities [3,8].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Yang

Sha

2020

Land

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Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

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Drought response of urban trees and turfgrass using airborne imaging spectroscopy

Cited by 42 publications

References 81 publications

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

Satellites to Sprinklers: Assessing the Role of Climate and Land Cover Change on Patterns of Urban Outdoor Water Use

Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

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