Algorithm for assessing forest stand productivity index using leaf area index

This paper describes a development of an algorithm for assessing stand productivity by considering the stand variables. Forest stand productivity is one of the crucial information that required to establish the business plan for unit management at the beginning of forest planning activity. The main study objective is to find out the most significant and accurate variable combination to be used for assessing the forest stand productivity, as well as to develop productivity estimation model based on leaf area index. The study found the best stand variable combination in assessing stand productivity were density of poles (X2), volume of commercial tree having diameter at breast height (dbh) 20-40 cm (X16), basal area of commercial tree of dbh >40 cm (X20) with Kappa Accuracy of 90.56% for classifying into 5 stand productivity classes. It was recognized that the examined algorithm provides excellent accuracy of 100% when the stand productivity was classified into only 3 classes. The best model for assessing the stand productivity index with leaf area index is y = 0.6214x - 0.9928 with R2= 0.71, where y is productivity index and x is leaf area index.

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Leaf area index estimation of even-aged oak (Quercus petraea) forests using in situ stand dendrometric parameters

10.1101/2021.08.05.454476 ◽

2021 ◽

Author(s):

Briere Maxime ◽

Christophe Francois ◽

Francois Lebourgeois ◽

Ingrid Seynave ◽

Francois Ningre ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Basal Area ◽

Quercus Petraea ◽

Forest Stand ◽

Stand Age ◽

Estimation Errors ◽

Data Set ◽

Area Index ◽

Net Productivity

The leaf area index (LAI) is a key characteristic of forest stand aboveground net productivity (ANP), and many methods have been developed to estimate the LAI. However, every method has flaws, e.g., methods may be destructive, require means or time and/or show intrinsic bias and estimation errors. A relationship using basal area (G) and stand age to estimate LAI was proposed by Sonohat et al. (2004). We used literature data in addition to data form measurements campaign made in the northern half of France to build a data set with large ranges of pedoclimatic conditions, stand age and measured LAI. We validated the Sonohat et al. (2004) relationship and attempted to improve or modify it using other stand/dendrometric characteristics that could be predictors of the LAI. The result is a series of three models using the G, age and/or quadratic mean diameter (Dg), and the models were able to estimate the LAI of an oak only even-aged forest stand with good confidence (root mean square error, RMSE < 0.75) While G is the main predictor here, age and Dg could be used conjointly or exclusively given the available data, with variable precision in the estimations. Although these models could not, by construction, relate to the interannual variability of the LAI, they may provide the theoretical LAI of an untouched forest (no meteorological, biotic or anthropogenic perturbation) in recent years. additionally, the use of this model may be more interesting than an LAI measurement campaign, depending on the means to be invested in such a campaign.

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A PROPOSED METHODOLOGY FOR THE CORRECTION OF THE LEAF AREA INDEX MEASURED WITH A CEPTOMETER FOR PINUS AND EUCALYPTUS FORESTS

Revista Árvore ◽

10.1590/0100-67622016000500008 ◽

2016 ◽

Vol 40 (5) ◽

pp. 845-854 ◽

Cited By ~ 2

Author(s):

Domingos Mendes Lopes ◽

Nigel Walford ◽

Helder Viana ◽

Carlos Roberto Sette Junior

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Correction Factor ◽

Basal Area ◽

Allometric Equations ◽

Nutrient Cycle ◽

Area Index ◽

Physiological Processes ◽

The Difference ◽

Non Destructive

ABSTRACT Leaf area index (LAI) is an important parameter controlling many biological and physiological processes associated with vegetation on the Earth's surface, such as photosynthesis, respiration, transpiration, carbon and nutrient cycle and rainfall interception. LAI can be measured indirectly by sunfleck ceptometers in an easy and non-destructive way but this practical methodology tends to underestimated when measured by these instruments. Trying to correct this underestimation, some previous studies heave proposed the multiplication of the observed LAI value by a constant correction factor. The assumption of this work is LAI obtained from the allometric equations are not so problematic and can be used as a reference LAI to develop a new methodology to correct the ceptometer one. This new methodology indicates that the bias (the difference between the ceptometer and the reference LAI) is estimated as a function of the basal area per unit ground area and that bias is summed to the measured value. This study has proved that while the measured Pinus LAI needs a correction, there is no need for that correction for the Eucalyptus LAI. However, even for this last specie the proposed methodology gives closer estimations to the real LAI values.

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Study on the applicability of leaf area index estimation model in an aspen forest over a growth period

Acta Ecologica Sinica ◽

10.5846/stxb201410212064 ◽

2016 ◽

Vol 36 (8) ◽

Author(s):

王龑 WANG Yan ◽

田庆久 TIAN Qingjiu ◽

王琦 WANG Qi ◽

王磊 WANG Lei

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Growth Period ◽

Estimation Model ◽

Area Index ◽

Aspen Forest ◽

Index Estimation

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High Spatial Resolution Optical Image Texture for Improved Estimation of Forest Stand Leaf Area Index

Canadian Journal of Remote Sensing ◽

10.1080/07038992.1996.10874668 ◽

1996 ◽

Vol 22 (4) ◽

pp. 441-449 ◽

Cited By ~ 41

Author(s):

M. A. Wulder ◽

S. E. Franklin ◽

M. B. Lavigne

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Forest Stand ◽

Optical Image ◽

Image Texture ◽

Area Index

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Spatial and seasonal variations of leaf area index (LAI) in subtropical secondary forests related to floristic composition and stand characters

10.5194/bg-2016-5 ◽

2016 ◽

Author(s):

Wenjuan Zhu ◽

Wenhua Xiang ◽

Qiong Pan ◽

Yelin Zeng ◽

Shuai Ouyang ◽

...

Keyword(s):

Spatial Heterogeneity ◽

Leaf Area Index ◽

Leaf Area ◽

Seasonal Variations ◽

Basal Area ◽

Floristic Composition ◽

Controlling Factors ◽

Forest Types ◽

Area Index ◽

Subtropical Forests

Abstract. Leaf area index (LAI) is an important parameter related to carbon, water and energy exchange between canopy and atmosphere, and is widely applied in the process models to simulate production and hydrological cycle in forest ecosystems. However, fine-scale spatial heterogeneity of LAI and its controlling factors have not been fully understood in Chinese subtropical forests. We used hemispherical photography to measure LAI values in three subtropical forests (i.e. Pinus massoniana – Lithocarpus glaber coniferous and evergreen broadleaved mixed forests, Choerospondias axillaris deciduous broadleaved forests, and L. glaber – Cyclobalanopsis glauca evergreen broadleaved forests) during period from April, 2014 to January, 2015. Spatial heterogeneity of LAI and its controlling factors were analysed by using geostatistics method the generalised additive models (GAMs), respectively. Our results showed that LAI values differed greatly in the three forests and their seasonal variations were consistent with plant phenology. LAI values exhibited strong spatial autocorrelation for three forests measured in January and for the L. glaber – C. glauca forest in April, July and October. Obvious patch distribution pattern of LAI values occurred in three forests during the non-growing period and this pattern gradually dwindled in the growing season. Stand basal area, crown coverage, crown width, proportion of deciduous species on basal area basis and forest types affected the spatial variations in LAI values in January, while species richness, crown coverage, stem number and forest types affected the spatial variations in LAI values in July. Floristic composition, spatial heterogeneity and seasonal variations should be considered for sampling strategy in indirect LAI measurement and application of LAI to simulate functional processes in subtropical forests.

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Influence of phytotechnology on growth, production and leaf area index of black walnut (Juglans nigra L.) monocultures in Slovakia

Journal of Forest Science ◽

10.17221/4592-jfs ◽

2012 ◽

Vol 51 (No. 5) ◽

pp. 213-224

Author(s):

F. Tokár ◽

E. Krekulová

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Dry Matter ◽

Basal Area ◽

Black Walnut ◽

Juglans Nigra ◽

Total Production ◽

Area Index ◽

Growth Production ◽

Crown Thinning

In the paper we evaluate the influence of crown thinning with positive selection, different intensity (moderate PRP III and heavy PRP IV) and 5-year frequency on development of growth, production, quality and leaf area index of black walnut (Juglans nigra L.) monocultures growing on the series of three permanent research plots (PRP) Sikenica (Levice Forest Enterprise, Levice Forest District) as observed in 1978–2003. The trends of development of mean stem, basal area, standing volume and aboveground dendromass (in dry matter) were mainly influenced by heavy crown thinning. The index of growth was as follows: basal area 169.01%, standing volume 262.12%, aboveground dendromass (in dry matter) 324.48%. At the age of 64 years the black walnut monocultures tended by heavy crown thinning had the following parameters: basal area 31.03 m2/ha, standing volume 463.88 m3/ha and aboveground dendromass 194.98 t/ha. Mean periodic increment reached the values: basal area 0.51 m2/ha/year, standing volume 11.48 m3/ha/year and dendromass 5.39 t/ha/year. The index of increment percent growth was: basal area + 31.75%, growing stock + 30.85% and dendromass + 0.79%, compared to the control. The total production was also significantly influenced by heavy thinning. At the stand age of 64 years the tended stands had the total basal area of 4.92 m2/ha, total volume production of 572.77 m3/ha and total weight production of 246.04 t/ha. The total mean increment of basal area is 0.67 m2/ha/year, of volume 8.95 m3/ha/year and of weight 3.84 t/ha/year, which is by 24.07%, 23.96% and 16.01% more than on the control plot. The leaf area index at the age of 64 years ranges from 6.54 ha/ha (PRP III) to 7.82 ha/ha (PRP V). Dendrochronological analyses revealed minimum widths of annual rings in the years 1952, 1961, 1968, 1971, 1975, 1981, 1983, 1985, 1993, 2000, maximum ones in 1951, 1957, 1959, 1967, 1974, 1979, 1982, 1984, 1989, 1999.

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A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images

Remote Sensing ◽

10.3390/rs13061140 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1140

Author(s):

Stephen M. Kinane ◽

Cristian R. Montes ◽

Timothy J. Albaugh ◽

Deepak R. Mishra

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Loblolly Pine ◽

Vegetation Indices ◽

Pine Plantations ◽

Estimation Model ◽

Moisture Index ◽

Modeling Framework ◽

Area Index ◽

Extensive Evaluation

Vegetation indices calculated from remotely sensed satellite imagery are commonly used within empirically derived models to estimate leaf area index in loblolly pine plantations in the southeastern United States. The data used to parameterize the models typically come with observation errors, resulting in biased parameters. The objective of this study was to quantify and reduce the effects of observation errors on a leaf area index (LAI) estimation model using imagery from Landsat 5 TM and 7 ETM+ and over 1500 multitemporal measurements from a Li-Cor 2000 Plant Canopy Analyzer. Study data comes from a 16 quarter 1 ha plot with 1667 trees per hectare (2 m × 3 m spacing) fertilization and irrigation research site with re-measurements taken between 1992 and 2004. Using error-in-variable methods, we evaluated multiple vegetation indices, calculated errors associated with their observations, and corrected for them in the modeling process. We found that the normalized difference moisture index provided the best correlation with below canopy LAI measurements (76.4%). A nonlinear model that accounts for the nutritional status of the stand was found to provide the best estimates of LAI, with a root mean square error of 0.418. The analysis in this research provides a more extensive evaluation of common vegetation indices used to estimate LAI in loblolly pine plantations and a modeling framework that extends beyond the typical linear model. The proposed model provides a simple to use form allowing forest practitioners to evaluate LAI development and its uncertainty in historic pine plantations in a spatial and temporal context.

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Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice

Remote Sensing ◽

10.3390/rs13010084 ◽

2020 ◽

Vol 13 (1) ◽

pp. 84

Author(s):

Tomoaki Yamaguchi ◽

Yukie Tanaka ◽

Yuto Imachi ◽

Megumi Yamashita ◽

Keisuke Katsura

Keyword(s):

Deep Learning ◽

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Regression Models ◽

Estimation Model ◽

Area Index ◽

Aerial Vehicle ◽

Rgb Images ◽

Multispectral Camera

Leaf area index (LAI) is a vital parameter for predicting rice yield. Unmanned aerial vehicle (UAV) surveillance with an RGB camera has been shown to have potential as a low-cost and efficient tool for monitoring crop growth. Simultaneously, deep learning (DL) algorithms have attracted attention as a promising tool for the task of image recognition. The principal aim of this research was to evaluate the feasibility of combining DL and RGB images obtained by a UAV for rice LAI estimation. In the present study, an LAI estimation model developed by DL with RGB images was compared to three other practical methods: a plant canopy analyzer (PCA); regression models based on color indices (CIs) obtained from an RGB camera; and vegetation indices (VIs) obtained from a multispectral camera. The results showed that the estimation accuracy of the model developed by DL with RGB images (R2 = 0.963 and RMSE = 0.334) was higher than those of the PCA (R2 = 0.934 and RMSE = 0.555) and the regression models based on CIs (R2 = 0.802-0.947 and RMSE = 0.401–1.13), and comparable to that of the regression models based on VIs (R2 = 0.917–0.976 and RMSE = 0.332–0.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.

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The role of stand density on growth efficiency, leaf area index, and resin flow in southwestern ponderosa pine forests

Canadian Journal of Forest Research ◽

10.1139/x06-233 ◽

2007 ◽

Vol 37 (2) ◽

pp. 343-355 ◽

Cited By ~ 63

Author(s):

Nate G. McDowell ◽

Henry D. Adams ◽

John D. Bailey ◽

Thomas E. Kolb

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Ponderosa Pine ◽

Basal Area ◽

Stand Density ◽

Growth Efficiency ◽

Resin Flow ◽

Area Index ◽

Sapwood Area ◽

Ponderosa Pine Forests

We examined the response of growth efficiency (GE), leaf area index (LAI), and resin flow (RF) to stand density manipulations in ponderosa pine ( Pinus ponderosa Dougl. ex Laws.) forests of northern Arizona, USA. The study used a 40 year stand density experiment including seven replicated basal area (BA) treatments ranging from 7 to 45 m2·ha–1. Results were extended to the larger region using published and unpublished datasets on ponderosa pine RF. GE was quantified using basal area increment (BAI), stemwood production (NPPs), or volume increment (VI) per leaf area (Al) or sapwood area (As). GE per Al was positively correlated with BA, regardless of numerator (BAI/Al, NPPs/Al, and VI/Al; r2 = 0.84, 0.95, and 0.96, respectively). GE per As exhibited variable responses to BA. Understory LAI increased with decreasing BA; however, total (understory plus overstory) LAI was not correlated with BA, GE, or RF. Opposite of the original research on this subject, resin flow was negatively related to GE per Al because Al/As ratios decline with increasing BA. BAI, and to a lesser degree BA, predicted RF better than growth efficiency, suggesting that the simplest measurement with the fewest assumptions (BAI) is also the best approach for predicting RF.

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