scholarly journals LaiPen LP 100 – a new device for estimating forest ecosystem leaf area index compared to the etalon: A methodologic case study

2018 ◽  
Vol 64 (No. 11) ◽  
pp. 455-468
Author(s):  
Jakub Černý ◽  
Jan Krejza ◽  
Radek Pokorný ◽  
Pavel Bednář
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Methods ◽  
Ecological Studies ◽  
Area Index ◽  
New Device ◽  
Area Efficiency ◽  
Wide Range ◽  
World Standard

Fast and precise leaf area index (LAI) estimation of a forest stand is frequently needed for a wide range of ecological studies. In the presented study, we compared side-by-side two instruments for performing LAI estimation (i.e. LaiPen LP 100 as a “newly developed device” and LAI-2200 PCA as the “world standard”), both based on indirect optical methods for performing LAI estimation in pure Norway spruce (Picea abies (Linnaeus) H. Karsten) stands under different thinning treatments. LAI values estimated by LaiPen LP 100 were approximate 5.8% lower compared to those measured by LAI-2200 PCA when averaging all collected data regardless of the thinning type. Nevertheless, when we considered the differences among LAI values at each measurement point within a regular grid, LaiPen LP 100 overestimated LAI values compared to those from LAI-2200 PCA on average by 1.4%. Therefore, both instruments are comparable. Similar LAI values between thinning from above (A) and thinning from below (B) approaches were indirectly detected by both instruments. The highest values of canopy production index and leaf area efficiency were observed within the stand thinned from above (plot A).

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.

scholarly journals Method comparison of indirect assessments of understory leaf area index (LAIu): A case study across the extended network of ICOS forest ecosystem sites in Europe

2021 ◽  
Vol 128 ◽  
pp. 107841
Author(s):  
Jan-Peter George ◽  
Wei Yang ◽  
Hideki Kobayashi ◽  
Tobias Biermann ◽  
Arnaud Carrara ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Ecosystem ◽  
Method Comparison ◽  
Area Index

Estimation of leaf area index using WorldView-2 and Aster satellite image: a case study from Turkey

2017 ◽  
Vol 189 (11) ◽  
Author(s):  
Alkan Günlü ◽  
Sedat Keleş ◽  
İlker Ercanlı ◽  
Muammer Şenyurt
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Satellite Image ◽  
Area Index
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