scholarly journals Leaf Area and Structural Changes after Thinning in Even-AgedPicea rubensandAbies balsameaStands in Maine, USA

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
R. Justin DeRose ◽  
Robert S. Seymour

We tested the hypothesis that changes in leaf area index (LAI m2 m−2) and mean stand diameter following thinning are due to thinning type and residual density. The ratios of pre- to postthinning diameter and LAI were used to assess structural changes between replicated crown, dominant, and low thinning treatments to 33% and 50% residual density in even-agedPicea rubensandAbies balsameastands with and without a precommercial thinning history in Maine, USA. Diameter ratios varied predictably by thinning type: low thinnings were <0.7, crown thinnings were >0.7 but <1.0, and dominant thinnings were >1.0 . LAI change was affected by type and intensity of thinning. On average, 33% density reduction removed <50% of LAI, whereas 50% density reduction removed >50% of LAI. Overall reduction of LAI was generally greatest in dominant thinnings (54%), intermediate in crown thinnings (46%), and lowest in low thinnings (35%). Upon closer examination by crown classes, the postthinning distribution of LAI between upper and lower crown classes varied by thinning history, thinning method, and amount of density reduction.

2010 ◽  
Vol 40 (4) ◽  
pp. 629-637 ◽  
Author(s):  
R. Justin DeRose ◽  
Robert S. Seymour

Leaf area index (LAI) strongly controls forest stand production. Silviculturists can easily manage this biologically important variable by quantifying its relationship to more directly manageable stand elements, such as density. Hypothesized patterns of LAI development over relative stand density (RD) in even-aged stands of balsam fir ( Abies balsamea (L.) Mill.) and red spruce ( Picea rubens Sarg.) were examined using 78 plots from the Cooperative Forestry Research Unit’s Commercial Thinning Research Network located in the Acadian forest zone in Maine. Nonlinear regression indicated that LAI was significantly related to RD, site quality, and stand top height. LAI increased nonlinearly with increasing RD holding stand top height constant. At a given RD, LAI peaked at approximately 13 m in stand top height. Site quality positively and linearly influenced LAI, but this was only apparent after crown closure, which in turn was influenced by initial stand density. Five-year trajectories of LAI–RD remeasurement data showed an increase in LAI and RD for all stands that varied by stand top height and site quality. Taken together, RD, stand top height, and site quality are strong predictors of LAI and can be used by silviculturists to manage for LAI.


2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim

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.


2021 ◽  
Vol 54 (3) ◽  
pp. 231-243
Author(s):  
Chao Liu ◽  
Zhenghua Hu ◽  
Rui Kong ◽  
Lingfei Yu ◽  
Yuanyuan Wang ◽  
...  

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 208
Author(s):  
Daniel Queirós da Silva ◽  
André Silva Aguiar ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Danilo Rabino ◽  
...  

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.


2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.


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