Canopy leaf area index and litter fall in stands of the mangrove Rhizophora apiculata of different age in the Mekong Delta, Vietnam

2000 ◽  
Vol 66 (4) ◽  
pp. 311-320 ◽  
Author(s):  
Barry Clough ◽  
Dang Trung Tan ◽  
Do Xuan Phuong ◽  
Dang Cong Buu
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Mekong Delta ◽  
Litter Fall ◽  
Area Index ◽  
Rhizophora Apiculata

A comparison of four methods for determining leaf area index in successional hardwood forests

1985 ◽  
Vol 15 (6) ◽  
pp. 1154-1158 ◽  
Author(s):  
Thomas W. Jurik ◽  
George M. Briggs ◽  
David M. Gates
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Hardwood Forests ◽  
Litter Fall ◽  
Area Index ◽  
Tree Diameter ◽  
Leaf Mass ◽  
The Vertical Distribution ◽  
Harvest Method ◽  
Do So

Four methods of determining leaf area index of three successional hardwood forests in northern lower Michigan were compared. Direct harvests gave values for leaf area index ranging from 1.4 to 3.6. Estimates of leaf area index derived from litter fall data were consistently higher than the harvest values and were highly dependent on the ratio of leaf area to leaf mass, which had to be estimated. A visual method using sightings through a tube gave values consistently lower (by 27–42%) than the harvest values. Calculations of leaf area index based on regressions of leaf mass versus tree diameter gave results very close to the harvest values for each site as a whole; calculations for smaller plots were more variable. The harvest method allowed measurement of the vertical distribution of leaf area; the other methods could not do so.

Vertical leaf area distribution, light transmittance, and application of the Beer–Lambert Law in four mature hardwood stands in the southern Appalachians

1995 ◽  
Vol 25 (6) ◽  
pp. 1036-1043 ◽  
Author(s):  
James M. Vose ◽  
Barton D. Clinton ◽  
Neal H. Sullivan ◽  
Paul V. Bolstad
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Independent Set ◽  
Light Transmittance ◽  
Litter Fall ◽  
Validation Data ◽  
Area Index ◽  
Vertical Leaf ◽  
Area Distribution ◽  
Leaf Area Distribution

We quantified stand leaf area index and vertical leaf area distribution, and developed canopy extinction coefficients (k), in four mature hardwood stands. Leaf area index, calculated from litter fall and specific leaf area (c2•g−1), ranged from 4.3 to 5.4 m2•m−2. In three of the four stands, leaf area was distributed in the upper canopy. In the other stand, leaf area was uniformly distributed throughout the canopy. Variation in vertical leaf area distribution was related to the size and density of upper and lower canopy trees. Light transmittance through the canopies followed the Beer–Lambert Law, and k values ranged from 0.53 to 0.67. Application of these k values to an independent set of five hardwood stands with validation data for light transmittance and litter-fall leaf area index yielded variable results. For example, at k = 0.53, calculated leaf area index was within ± 10% of litter-fall estimates for three of the five sites, but from −35 to + 85% different for two other sites. Averaged across all validation sites, litter-fall leaf area index and Beer-Lambert leaf area index predictions were in much closer agreement ( ± 7 to ± 15%).

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.

Sign in / Sign up

Export Citation Format

Share Document