scholarly journals Seasonal influence of leaf area index (LAI) on the energy performance of a green facade

2021 ◽  
pp. 108497
Author(s):  
Gabriel Pérez ◽  
Julià Coma ◽  
Marta Chàfer ◽  
Luisa F. Cabeza
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Energy Performance ◽  
Seasonal Influence ◽  
Area Index ◽  
Green Façade ◽  
Green Facade

scholarly journals Effects of Extensive Green Roofs on Energy Performance of School Buildings in Four North American Climates

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 6 ◽  
Author(s):  
Milad Mahmoodzadeh ◽  
Phalguni Mukhopadhyaya ◽  
Caterina Valeo
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Height ◽  
Green Roof ◽  
Green Roofs ◽  
Energy Performance ◽  
Design Parameters ◽  
Soil Thickness ◽  
Area Index ◽  
Roof Design

A comprehensive parametric analysis was conducted to evaluate the influence of the green roof design parameters on the thermal or energy performance of a secondary school building in four distinctively different climate zones in North America (i.e., Toronto, ON, Canada; Vancouver, BC, Canada; Las Vegas, NV, USA and Miami, FL, USA). Soil moisture content, soil thermal properties, leaf area index, plant height, leaf albedo, thermal insulation thickness and soil thickness were used as design variables. Optimal parameters of green roofs were found to be functionally related to meteorological conditions in each city. In terms of energy savings, the results showed that the light-weight substrate had better thermal performance for the uninsulated green roof. Additionally, the recommended soil thickness and leaf area index for all four cities were 15 cm and 5 respectively. The optimal plant height for the cooling dominated climates is 30 cm and for the heating dominated cities is 10 cm. The plant albedo had the least impact on the energy consumption while it was effective in mitigating the heat island effect. Finally, unlike the cooling load, which was largely influenced by the substrate and vegetation, the heating load was considerably affected by the thermal insulation instead of green roof design parameters.

Characterizing leaf area index of rooftop farm to assess thermal-energy performance by simulation

2021 ◽  
pp. 110960
Author(s):  
Eduardo Grala da Cunha ◽  
Celina Maria Brito Correa ◽  
Roberta Peil ◽  
Viviane Mülech Ritter ◽  
Daniela Hohn ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Thermal Energy ◽  
Energy Performance ◽  
Area Index

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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