Ahmad, I., Singh, A., Fahad, M., & Waqas, M. M. (2020). Remote sensing-based framework to predict and assess the interannual variability of maize yields in Pakistan using Landsat imagery. Computers and Electronics in Agriculture, 178.
Abstract: Predicting crop yields and their spatio-temporal variability under a changing climate is a challenging but essential undertaking for crop management and policymaking purposes. The availability of information on risks associated with effects of climatic variability on agricultural activity outcomes is critical for stakeholders ranging from individual landowners to national economists alike. This research was conducted as a pilot study to (1) develop satellite remote sensing based estimates of maize acreage in a typical Maize growing region in Pakistan, (2) to develop a statistical-empirical model for prediction of maize yields, and finally, (3) to assess the influence of temperature on inter-annual variability in maize yields across a decade. A total of eight machine learning algorithms were tested for identifying maize growing operations in the Faisalabad district of Pakistan using Landsat 8 imagery. Classification models were evaluated via 200 randomly selected ground-verified points across the study region. Results of the maize mapping exercise were used to estimate interannual maize yields using Landsat-derived multi-temporal normalized difference vegetation index (NDVI) and land surface temperature (LST) data as predictors. Predictors for the yield forecasting model were selected via principal component screening and were fed into a least absolute shrinkage and selection (LASSO) regression model. The yield model thus developed was applied to 10 years of past data (2006-2017) and validated against data recorded by government sources. Finally, predictions spanning the ten years were tested for effects of temperature variability to find evidence of influence of ambient temperature on maize yields. Results indicate that support vector machine classifiers work the best in this landscape (accuracies >90%) and reveal that maize cropping area may be underestimated in government sources by as much as 14%. The LASSO regression models also showed very good fits (validation R2 = 0.95) and were fairly accurate in tracking interannual variations in maize yields (R2 = 0.78.) Results also indicate that the maximum temperature has significant negative influence (R2 = 0.76, P < 0.0001) on maize yields in Faisalabad district. Methods presented in this study should be of use to policymakers for better formulating export-import policies and decisions governing food security issues in the larger region.
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Peng, J., Ma, J., Liu, Q., Liu, Y., Hu, Y. 'na, Li, Y., et al. (2018). Spatial-temporal change of land surface temperature across 285 cities in China: An urban-rural contrast perspective. Sci Total Environ, .
Abstract: As an important theme in global climate change and urban sustainable development, the changes of land surface temperature (LST) and surface urban heat island (SUHI) have been more and more focused by urban ecologists. This study used land-use data to identify the urban-rural areas in 285 cities in China and comparatively analyzed LST in urban-rural areas with the perspective of spatial-temporal dynamics heterogeneity. The results showed that, 98.9% of the cities exhibited SUHI effect in summer nighttime and the effect was stronger in northern cities than that in southern cities. In 2010, the mean SUHI intensity was the largest in summer daytime, with 4.6% of the cities having extreme SUHI of over 4 degrees C. From 2001 to 2010, the nighttime LST of most cities increased more quickly in urban areas compared with rural areas, with an increasing tendency of the urban-rural LST difference. The difference in the urban- rural LST change rate was concentrated in the range of 0-0.1 degrees C/year for 68.0% of cities in winter and 70.8% of cities in summer. For the higher LST increasing in urban areas compared with rural areas, there were more cities in summer than winter, indicating that the summer nighttime was the key temporal period for SUHI management. Based on the change slope of urban-rural LST, cities were clustered into four types and the vital and major zones for urban thermal environment management were identified in China. The vital zone included cities in Hunan, Hubei and other central rising provinces as well as the Beibu Gulf of Guangxi Province. The major zone included most of the cities in Central Plain Urban Agglomeration, Yangtze River Delta and Pearl River Delta. These results can provide scientific basis for SUHI adaptation in China.
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Yu, Q., Ji, W., Pu, R., Landry, S., Acheampong, M., O Neil-Dunne, J., et al. (2020). A preliminary exploration of the cooling effect of tree shade in urban landscapes. International Journal of Applied Earth Observation and Geoinformation, 92, 102161.
Abstract: Mitigating urban heat island (UHI) effects, especially under climate change, is necessary for the promotion of urban sustainability. Shade is one of the most important functions provided by urban trees for mitigating UHI. However, the cooling effect of tree shade has not been adequately investigated. In this study, we used a simple and straightforward method to quantify the spatial and temporal variation of tree shade and examined its effect on land surface temperature (LST). We used the hillshade function in a geographic information system to quantify the spatiotemporal patterns of tree shade by integrating sun location and tree height. Relationships between shade and LST were then compared in two cities, Tampa, Florida and New York City (NYC), New York. We found that: (1) Hillshade function combining the sun location and tree height can accurately capture the spatial and temporal variation of tree shade; (2) Tree shade, particularly at 07:30, has significant cooling effect on LST in Tampa and NYC; and (3) Shade has a stronger cooling effect in Tampa than in NYC, which is most likely due to the differences in the ratio of tree canopy to impervious surface cover, the spatial arrangements of trees and buildings, and their relative heights. Comparing the cooling effects of tree shade in two cities, this study provides important insights for urban planners for UHI mitigation in different cities.
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Yu, Q., Ji, W., Pu, R., Landry, S., Acheampong, M., O Neil-Dunne, J., et al. (2020). A preliminary exploration of the cooling effect of tree shade in urban landscapes. Intl J Applied Earth Observation Geoinformation, 92.
Abstract: Mitigating urban heat island (UHI) effects, especially under climate change, is necessary for the promotion of urban sustainability. Shade is one of the most important functions provided by urban trees for mitigating UHI. However, the cooling effect of tree shade has not been adequately investigated. In this study, we used a simple and straightforward method to quantify the spatial and temporal variation of tree shade and examined its effect on land surface temperature (LST). We used the hillshade function in a geographic information system to quantify the spatiotemporal patterns of tree shade by integrating sun location and tree height. Relationships between shade and LST were then compared in two cities, Tampa, Florida and New York City (NYC), New York. We found that: (1) Hillshade function combining the sun location and tree height can accurately capture the spatial and temporal variation of tree shade; (2) Tree shade, particularly at 07:30, has significant cooling effect on LST in Tampa and NYC; and (3) Shade has a stronger cooling effect in Tampa than in NYC, which is most likely due to the differences in the ratio of tree canopy to impervious surface cover, the spatial arrangements of trees and buildings, and their relative heights. Comparing the cooling effects of tree shade in two cities, this study provides important insights for urban planners for UHI mitigation in different cities.
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