Effects of species composition, management intensity, and shade tolerance on vertical distribution of leaf area index in juvenile stands in Maine, USA

2014 ◽  
Vol 134 (2) ◽  
pp. 281-291 ◽  
Author(s):  
Andrew S. Nelson ◽  
Robert G. Wagner ◽  
Aaron R. Weiskittel ◽  
Michael R. Saunders
Keyword(s):  
Species Composition ◽  
Vertical Distribution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Shade Tolerance ◽  
Area Index ◽  
Management Intensity

Vertical distribution of leaf area in Larixoccidentalis: a comparison of two estimation methods

1989 ◽  
Vol 19 (9) ◽  
pp. 1131-1136 ◽  
Author(s):  
William R. Bidlake ◽  
R. Alan Black
Keyword(s):  
Vertical Distribution ◽  
Leaf Area Index ◽  
Normal Distribution ◽  
Leaf Area ◽  
Stand Density ◽  
Total Leaf Area ◽  
Area Index ◽  
Total Leaf ◽  
Gap Fraction ◽  
The Vertical Distribution

Total leaf-area index and the vertical distribution of leaf-area index were described for an unthinned stand (density 11 250 stems/ha) and a thinned stand (density 1660 stems/ha) of 30-year-old Larixoccidentalis Nutt. Two independent methods were used to estimate leaf-area index in each of the two stands. The first method is based on allometric relationships that are applied to stem measurements, and the second method is based on gap-fraction analysis of fisheye photographs. Leaf-area index estimates obtained by the two methods were not significantly different. The gap-fraction method provides a desirable alternative because much less fieldwork is required, however, use of this method is limited to canopies where the light-blocking elements are randomly displayed. Total leaf-area index values for the unthinned and thinned stands were 5.0 and 3.6, respectively. The vertical distribution of leaf-area index in the unthinned stand resembled a normal distribution. The vertical distribution of leaf-area index in the thinned stand would have resembled a normal distribution, except that thinning operations resulted in a truncated distribution of leaf-area index at the canopy base.

scholarly journals Ground-based estimation of leaf area index and vertical distribution of leaf area density in a Betula ermanii forest

Silva Fennica ◽  
2009 ◽  
Vol 43 (5) ◽  
Author(s):  
Akihiro Sumida ◽  
Taro Nakai ◽  
Masahito Yamada ◽  
Kiyomi Ono ◽  
Shigeru Uemura ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Area Density ◽  
Betula Ermanii ◽  
Leaf Area Density ◽  
Betula Ermanii Forest

Estimation of Forest Leaf Area Index from Remote Sensing Data Using the Algorithm Based on Geometric-optical Model

2012 ◽  
Vol 14 (3) ◽  
pp. 358-365
Author(s):  
Hanyue CHEN ◽  
Wenjiang HUANG ◽  
Zheng NIU ◽  
Shuai GAO
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Model ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data

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.

Hyperspectral characteristics and leaf area index monitoring of rice (Oryza sativa L.) under carbon dioxide concentration enrichment

2021 ◽  
Vol 54 (3) ◽  
pp. 231-243
Author(s):  
Chao Liu ◽  
Zhenghua Hu ◽  
Rui Kong ◽  
Lingfei Yu ◽  
Yuanyuan Wang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Oryza Sativa ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Oryza Sativa L ◽  
Carbon Dioxide Concentration ◽  
Area Index
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