Performance of the LAI-2000 plant canopy analyzer in estimating leaf area index of some Scots pine stands

1994 ◽  
Vol 14 (7-8-9) ◽  
pp. 981-995 ◽  
Author(s):  
P. Stenberg ◽  
S. Linder ◽  
H. Smolander ◽  
J. Flower-Ellis
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Scots Pine ◽  
Plant Canopy ◽  
Area Index ◽  
Pine Stands ◽  
Scots Pine Stands

Application of a forest reflectance model in estimating leaf area index of Scots pine stands using Landsat-7 ETM reflectance data

2003 ◽  
Vol 29 (3) ◽  
pp. 314-323 ◽  
Author(s):  
Miina Rautiainen ◽  
Pauline Stenberg ◽  
Tiit Nilson ◽  
Andres Kuusk ◽  
Heikki Smolander
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Scots Pine ◽  
Area Index ◽  
Reflectance Model ◽  
Landsat 7 ◽  
Pine Stands ◽  
Reflectance Data ◽  
Scots Pine Stands

scholarly journals Performance of LAI-2200 Plant Canopy Analyzer on Leaf Area Index of Jatropha Nut Estimation

2016 ◽  
Vol 15 (4) ◽  
pp. 191-197 ◽  
Author(s):  
B.P. Lena ◽  
M.V. Folegatti ◽  
J.P. Francisco ◽  
O.N.A. Santos ◽  
I.P.S. Andrade
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Canopy ◽  
Area Index

scholarly journals Leaf area index of ground covers in a subtropical watershed

2003 ◽  
Vol 60 (3) ◽  
pp. 425-431 ◽  
Author(s):  
Alexandre Cândido Xavier ◽  
Carlos Alberto Vettorazzi
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Sugar Cane ◽  
Riparian Forest ◽  
Initial Step ◽  
Plant Canopy ◽  
Structural Variable ◽  
Forest Patches ◽  
Area Index ◽  
Plant Characteristics

Leaf Area Index (LAI), an important structural variable descriptive of vegetation, is directly related to evapotranspiration and productivity. The objective of this work was to measure and analyze monthly LAI of different ground covers in a subtropical watershed. A field campaign to collect monthly LAI data was carried out during the year 2001, with a LAI-2000 (plant canopy analyzer) device, in sugarcane, pasture, corn, eucalypt, and riparian forest patches. Riparian forest presented a maximum LAI of 4.90; LAI values decreased as precipitation decreased, as it is a characteristic of this type of semideciduous vegetation. LAI for sugar cane presented the greatest variability throughout the year, related to plant characteristics and crop management in the study area. Results represent an initial step for the understanding of LAI dynamics in the study area and areas under similar conditions.

scholarly journals A methodological approach for the assessment of basic crown parameters in Scots pine stands

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Vidas Stakėnas ◽  
Iveta Varnagirytė-Kabašinskienė ◽  
Audrius Kabašinskas
Keyword(s):  
Surface Area ◽  
Scots Pine ◽  
Measurement Techniques ◽  
Forest Monitoring ◽  
Tree Crown ◽  
Area Index ◽  
Wide Range ◽  
Crown Defoliation ◽  
Pine Stands ◽  
Scots Pine Stands

Among other measurement techniques applied for the assessment of leaf area index, direct methods are still valued as the most accurate measures and often implemented as calibration tools. Even though more attention has been given to indirect measurements of tree crown properties in forest ecosystems over the last decades, the present study was designed to discuss the direct (destructive) and indirect (non-destructive) methods used for the assessment of crown measures in the stands defoliated from 20 to 90%. The stands with similar stand characteristics and representing relatively wide range of defoliation served as an appropriate target for the assessment of foliage mass variations. Overall, this study showed that the foliage mass or its surface area and defoliation at the stand level can be determined by the conventional methods used for the assessment of defoliation in forest monitoring programme as well as the PAR transmission methods. The findings showed that needle surface area decreased with the increase of tree defoliation; however, the changes of branch and stem surface areas were insignificant. Otherwise, the branch and shoot area contribute significantly to the total vegetation surface area at least in Scots pine stands. This study also strengthened the idea that the indirect measurement of vegetation area index underestimated vegetation area index at least in Scots pine stands defoliated less than 60%. The multivariate regression models were developed using tree diameter at breast height and tree crown defoliation ranges to estimate needle surface area. Keywords: Pinus sylvestris, crown defoliation, needle area index, regression model

scholarly journals Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1027 ◽  
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Michal Jenicek
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Snow Depth ◽  
Control Point ◽  
Winter Season ◽  
Snow Accumulation ◽  
Plant Canopy ◽  
Coverage Area ◽  
Area Index ◽  
Uav Images

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

Leaf area index and canopy stratification in Scots pine ( Pinus sylvestris L.) stands

2002 ◽  
Vol 23 (18) ◽  
pp. 3605-3618 ◽  
Author(s):  
K. Soudani ◽  
J. Trautmann ◽  
J.-M. N. Walter
Keyword(s):  
Pinus Sylvestris ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Scots Pine ◽  
Area Index

scholarly journals Error Analysis of LAI Measurements with LAI-2000 Due to Discrete View Angular Range Angles for Continuous Canopies

2021 ◽  
Vol 13 (7) ◽  
pp. 1405
Author(s):  
Jun Geng ◽  
Gang Yuan ◽  
J. M. Chen ◽  
Chunguang Lyu ◽  
Lili Tu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Weight Coefficient ◽  
Plant Canopy ◽  
Discrete Form ◽  
Area Index ◽  
Gap Fraction ◽  
Inclination Angles ◽  
Leaf Inclination ◽  
Specific Error

As a widely used ground-based optical instrument, the LAI-2000 or LAI-2200 plant canopy analyzer (PCA) (Li-Cor, Inc., Lincoln, NE) is designed to measure the plant effective leaf area index (Le) by measuring the canopy gap fraction at several limited or discrete view zenith angles (VZAs) (usually five VZAs: 7, 23, 38, 53, and 68°) based on Miller’s equation. Miller’s equation requires the probability of radiative transmission through the canopy to be measured over the hemisphere, i.e., VZAs in the range from 0 to 90°. However, the PCA view angle ranges are confined to several limited ranges or discrete sectors. The magnitude of the error produced by the discretization of VZAs in the leaf area index measurements remains difficult to determine. In this study, a theoretical deduction was first presented to definitely prove why the limited or discrete VZAs or ranges can affect the Le measured with the PCA, and the specific error caused by the limited or discrete VZAs was described quantitatively. The results show that: (1) the weight coefficient of the last PCA ring is the main cause of the error; (2) the error is closely related to the leaf inclination angles (IAs)—the Le measured with the PCA can be significantly overestimated for canopies with planophile IAs, whereas it can be underestimated for erectophile IAs; and (3) the error can be enhanced with the increment of the discrete degree of PCA rings or VZAs, such as using four or three PCA rings. Two corrections for the error are presented and validated in three crop canopies. Interestingly, although the leaf IA type cannot influence the Le calculated by Miller’s equation in the hemispheric space, it affects the Le measured with the PCA using the discrete form of Miller’s equation for several discrete VZAs.

Sign in / Sign up

Export Citation Format

Share Document