Tree planting indices and their effects on summer park thermal environment: A case study of a subtropical satellite city, China

2020 ◽  
pp. 1420326X2097761
Author(s):  
Wei Guo ◽  
Bin Cheng ◽  
Chunlu Wang ◽  
Xinyu Tang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Thermal Environment ◽  
Urban Forestry ◽  
Urban Environments ◽  
Tree Planting ◽  
View Factor ◽  
Radiation Level ◽  
Area Index ◽  
Sky View Factor

Urban residents are suffering from serious thermal stress resulting from urban heat island and global warming. Investigators have explored several methods to address this issue. Vegetation, especially trees, were found to play a vital part in urban environments. It produces a cooling effect by reducing temperature and radiation levels. To find thermal performances of trees in detail, this study physically measured two urban parks during August 2019 in a satellite city of China regarding their thermal environments relevant to tree planting indices. There were three planting indices used, sky view factor, leaf area index and enclosure area. Through associating them with thermal indicators by linear regression, all of the indices were confirmed to have significant thermal effects. Every 0.1 increase in sky view factor resulted in an increase of 1°C air temperature, 0.16 m/s air velocity, 40 W/m2 solar radiation level and 1.6°C mean radiant temperature. Same effects were found in nearly 0.4 leaf area index decrease and approximately 20 m2 enclosure area increase. These results provide very optimistic directions for future urban forestry planning and landscaping.

Estimating Potato Tuber Yield in a Sub-tropical Environment with Simple Radiation-Based Models

1995 ◽  
Vol 31 (1) ◽  
pp. 65-73 ◽  
Author(s):  
S. S. Prihar ◽  
V. K. Arora ◽  
G. Singh ◽  
R. Singh
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Radiation Effects ◽  
Dry Matter ◽  
Tuber Yield ◽  
Tropical Environment ◽  
Dry Matter Accumulation ◽  
Linear Change ◽  
Radiation Level ◽  
Area Index

SummaryDry matter and tuber yields of potato grown in a sub-tropical environment were estimated employing simple radiation-based models which require meteorological information on air temperature and solar radiation. Two versions of the MacKerron and Waister (1985) model, in which estimation of dry matter accumulation relies on a single composite parameter, radiation use efficiency (RUE), were compared with the Versteeg and Van Keulen (1986) model, which explicitly accounts for temperature and radiation effects on dry matter accumulation. In the original version of the MacKerron and Waister model, a linear change in the radiation interception factor with leaf area index is assumed; in the modified version an exponential change in the interception factor with leaf area index is considered. The accumulation of dry matter estimated from all three models was close to the measured values throughout the growing season, but estimates of tuber yield differed widely. Our analysis showed that the best agreement with measured values was obtained using the MacKerron and Waister linear model with RUE values adjusted according to the incident radiation level.Estimatión del rendimiento del tubérculo de la patata

scholarly journals Leaf area index retrieval of urban vegetation based on sentinel-2 data in Zhongshan, South China

2020 ◽  
Vol 49 (4) ◽  
pp. 1103-1109
Author(s):  
Jiannan Cai ◽  
Qiancai Jiang ◽  
Yafei Shi ◽  
Jiabin Wang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
European Space Agency ◽  
Urban Forestry ◽  
Dynamic Monitoring ◽  
Free Access ◽  
Large Area ◽  
Area Index ◽  
Important Indicator ◽  
Sentinel 2

Leaf area index (LAI) is an important indicator of vegetation growth and health monitoring. The free access of Sentinel-2 optical satellite data from European Space Agency (ESA) since June 2015 made it possible to determine dynamic monitoring of LAI in a large area and a short re-entry period, owing to its high spatial resolution (up to 10 m) and unique band combination. These features of Sentinel-2 may bring great application potential in urban forestry management and ecological environment monitoring. In case of Zhongshan city in Guangdong Province, based on Sentinels Application Platform (SNAP) software and two scenes of Sentinel-2 data, LAI of the whole city, as well as specific areas, were retrieved and analyzed. The retrieval result was consistent with the ecological spatial pattern and the vegetation protection status in Zhongshan city, also highly consistent with the field LAI measurement in the forest in a reservoir. The result indicates that this method has good applicability and accuracy in urban LAI inversion.

scholarly journals Estimation of leaf area index in isolated trees with digital photography and its application to urban forestry

2015 ◽  
Vol 14 (2) ◽  
pp. 377-382 ◽  
Author(s):  
Francesco Chianucci ◽  
Nicola Puletti ◽  
Elena Giacomello ◽  
Andrea Cutini ◽  
Piermaria Corona
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Urban Forestry ◽  
Digital Photography ◽  
Area Index ◽  
Isolated Trees

scholarly journals Correlating the Sky View Factor with the Pedestrian Thermal Environment in a Hot Arid University Campus Plaza

2021 ◽  
Vol 13 (2) ◽  
pp. 468
Author(s):  
Randa Osama Shata ◽  
Ayman Hassaan Mahmoud ◽  
Mohammad Fahmy
Keyword(s):  
Thermal Environment ◽  
Field Measurements ◽  
Urban Trees ◽  
View Factor ◽  
University Campus ◽  
Physiological Equivalent Temperature ◽  
Area Index ◽  
Comfort Index ◽  
Sky View Factor ◽  
The Impact

In hot, arid regions on university campuses, students are more vulnerable to heat stresses than in street canyons in terms of function; however, the knowledge of the impact of built environments on thermal performance is still lacking. In two summer and winter days, the shading effect of the existing urban trees pattern in a university campus in Egypt was examined to correlate their Sky View Factor (SVF) with the thermal environment, meteorology, Physiological Equivalent Temperature (PET), and Universal Thermal Comfort Index (UTCI). The ENVI-met model was used in order to assess meteorological parameters, followed by SVF calculation in the Rayman program. Meteorological field measurements validated the simulation model and measured the Leaf Area Index (LAI) of two native urban trees to model the in-situ canopies foliage. In summer, the results showed a significant direct impact of the SVF on mean radiant temperature (Tmrt), PET, and UTCI; however, the excessive shading by trees on materials with a low albedo and low wind speed could lead to a slight increase in air temperature. Meanwhile, in the winter, SVF did not affect the microclimatic variables, PET, or UTCI. The resulting insight into the correlation between SVF and Tmrt emphasizes the importance of urban trees in modifying the microclimates of already-existing university plazas.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

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