scholarly journals Performance of Four Optical Methods in Estimating Leaf Area Index at Elementary Sampling Unit of Larix principis-rupprechtii Forests

Forests ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 30 ◽  
Author(s):  
Jie Zou ◽  
Yong Zuo ◽  
Peihong Zhong ◽  
Wei Hou ◽  
Peng Leng ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Global Climate ◽  
Structural Characteristics ◽  
Correction Method ◽  
Optical Methods ◽  
Sampling Unit ◽  
Observation System ◽  
Area Index ◽  
Inversion Model

Optical methods are frequently used as a routine method to obtain the elementary sampling unit (ESU) leaf area index (LAI) of forests. However, few studies have attempted to evaluate whether the ESU LAI obtained from optical methods matches the accuracy required by the LAI map product validation community. In this study, four commonly used optical methods, including digital hemispherical photography (DHP), digital cover photography (DCP), tracing radiation of canopy and architecture (TRAC) and multispectral canopy imager (MCI), were adopted to estimate the ESU (25 m × 25 m) LAI of five Larix principis-rupprechtii forests with contrasting structural characteristics. The impacts of three factors, namely, inversion model, canopy element or woody components clumping index ( Ω e or Ω w ) algorithm, and the woody components correction method, on the ESU LAI estimation of the four optical methods were analyzed. Then, the LAI derived from the four optical methods was evaluated using the LAI obtained from litter collection measurements. Results show that the performance of the four optical methods in estimating the ESU LAI of the five forests was largely affected by the three factors. The accuracy of the LAI obtained from the DHP and MCI strongly relied on the inversion model, the Ω e or Ω w algorithm, and the woody components correction method adopted in the estimation. Then the best Ω e or Ω w algorithm, inversion model and woody components correction method to be used to obtain the ESU LAI of L. principis-rupprechtii forests with the smallest root mean square error (RMSE) and mean absolute error (MAE) were identified. Amongst the three typical woody components correction methods evaluated in this study, the woody-to-total area ratio obtained from the destructive measurements is the most effective method for DHP to derive the ESU LAI with the smallest RMSE and MAE. In contrast, using the woody area index obtained from the leaf-off DHP or DCP images as the woody components correction method would result in a large LAI underestimation. TRAC and MCI outperformed DHP and DCP in the ESU LAI estimation of the five forests, with the smallest RMSE and MAE. All the optical methods, except DCP, are qualified to obtain the ESU LAI of L. principis-rupprechtii forests with an MAE of <20% that is required by the global climate observation system. None of the optical methods, except TRAC, show the potential to obtain the ESU LAI of L. principis-rupprechtii forests with an MAE of <5%.

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).

Effect of residual leaf area index on spatial components of Tifton 85 pastures and ingestive behaviour of sheep

2017 ◽  
Vol 57 (5) ◽  
pp. 903 ◽  
Author(s):  
W. L. Silva ◽  
J. P. R. Costa ◽  
G. P. Caputti ◽  
A. L. S. Valente ◽  
D. Tsuzukibashi ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Rainy Season ◽  
Structural Characteristics ◽  
Light Interception ◽  
Dry Season ◽  
Area Index ◽  
Dead Material ◽  
Point Quadrat ◽  
Ingestive Behaviour

This study compared the effect of residual leaf area index (rLAI) on the spatial distribution of morphological components of Tifton 85 (Cynodon spp.) pastures and the ingestive behaviour of grazing sheep. Also, it was investigated whether any specific correlation could be found between pasture structural characteristics and sheep ingestive behaviour. Four rLAI treatments (0.8; 1.4; 2.0 and 2.6) with four replications were evaluated per period. Sheep grazed under rotational stocking management and they grazed for 4 days in each pasture while pasture regrowth period was determined by the 95% light interception requirement. Pasture structure was evaluated using inclined point-quadrat, LAI estimates, light interception and leaf : stem ratio. The 2.6 rLAI yielded the highest proportion of dead material in the lower canopy. In the post-grazing period the proportion of leaves increased with increasing rLAI, especially on the canopy surface during the rainy season. In the pre-grazing average pasture height ranged between 19 and 26 cm with dead material and stem observed up to the canopy surface in the dry season. The animals grazed longer on the last day (89.72%) compared with the first day (80.25%) in the dry season. However, they spent less time (11.45%) ruminating in the dry season compared with the rainy season (15.38%), regardless of the grazing day. Grazing time decreased and rumination time increased as rLAI increased. Sheep grazing time correlated negatively with pasture height, before and after grazing. The sheep tend to graze longer on Tifton 85 pastures when rLAI was lower and forage supply was possibly less as on the last grazing day and in the dry season.

scholarly journals An inversion model for estimating the negative air ion concentration using MODIS images of the Daxing’anling region

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242554
Author(s):  
Cui Yue ◽  
Zhao Yuxin ◽  
Zhang Nan ◽  
Zhang Dongyou ◽  
Yang Jiangning
Keyword(s):  
Air Quality ◽  
Surface Temperature ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Area Index ◽  
Inversion Model ◽  
Forest Coverage ◽  
The Relationship

The negative air ion (NAI) concentration is an essential indicator of air quality and atmospheric pollution. The NAI concentration can be used to monitor air quality on a regional scale and is commonly determined using field measurements. However, obtaining these measurements is time-consuming. In this paper, the relationship between remotely sensed surface parameters (such as land surface temperature, normalized difference vegetation index (NDVI), and leaf area index) obtained from MODIS data products and the measured NAI concentration using a stepwise regression method was analyzed to estimate the spatial distribution of the NAI concentration and verify the precision. The results indicated that the NAI concentration had a negative correlation with temperature, leaf area index (LAI), and gross primary production while it exhibited a positive correlation with the NDVI. The relationship between land surface temperature and the NAI concentration in the Daxing’anling region is expressed by the regression equation of y = -35.51x1 + 11206.813 (R2 = 0.6123). Additionally, the NAI concentration in northwest regions with high forest coverage was higher than that in southeast regions with low forest coverage, suggesting that forests influence the air quality and reduce the impact of environmental pollution. The proposed inversion model is suitable for evaluating the air quality in Daxing’anling and provides a reference for air quality evaluation in other areas. In the future, we will expand the quantity and distribution range of sampling points, conduct continuous observations of NAI concentrations and environmental parameters in the research areas with different land-use types, and further improve the accuracy of inversion results to analyze the spatiotemporal dynamic changes in NAI concentration and explore the possibility of expanding the application areas of NAI monitoring.

On improving the accuracy of digital hemispherical photography measurements of seasonal leaf area index variation in deciduous broadleaf forests

2015 ◽  
Vol 45 (6) ◽  
pp. 721-731 ◽  
Author(s):  
Zhili Liu ◽  
Xingchang Wang ◽  
Jing M. Chen ◽  
Chuankuan Wang ◽  
Guangze Jin
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Changes ◽  
Optical Methods ◽  
Hemispherical Photography ◽  
Area Index ◽  
Clumping Index ◽  
Area Variation ◽  
Major Factors

Optical methods have been widely used to estimate seasonal changes of the leaf area index (LAI) in forest stands because they are convenient and effective; however, their accuracy in deciduous broadleaf forests has rarely been evaluated. We estimate the seasonal changes in the LAI by combining periodic observations of leaf area variation with litter collection (LAIdir) in deciduous broadleaf forests and use these estimates to evaluate the accuracy of optical LAI measurements made using digital hemispherical photography (DHP). We also propose a method to correct DHP-derived LAI (LAIDHP) values for seasonal changes in major factors that influence the determination of LAI, including the woody to total area ratio (α), the element clumping index (ΩE, using three different methods), and the photographic exposure setting (E). Before these corrections were made, LAIDHP underestimates LAIdir by 14%–55% from 21 May to 1 October but overestimates it by 78% on 12 May and by 226% on 11 October. Although pronounced differences are observed between LAIdir and LAIDHP, they are significantly correlated (R2 = 0.85, RMSE = 0.32, P < 0.001). After considering seasonal changes in α, ΩE, and E, the accuracy of LAIDHP improves markedly, with a mean difference between the corrected LAIDHP and LAIdir of <17% in all periods. The results suggest that the proposed scheme for correcting LAIDHP is useful and effective for estimating seasonal LAI variation in deciduous broadleaf forests.

scholarly journals Comparison of LiDAR-based Models for True Leaf Area Index and Effective Leaf Area Index Estimation in Young Beech Forests

2020 ◽  
Vol 68 (3) ◽  
pp. 559-566
Author(s):  
Zdeněk Patočka ◽  
Kateřina Novosadová ◽  
Pavel Haninec ◽  
Radek Pokorný ◽  
Tomáš Mikita ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Regression Models ◽  
Canopy Structure ◽  
Optical Methods ◽  
Distribution Model ◽  
Area Index ◽  
Index Estimation ◽  
Effective Leaf Area Index

The leaf area index (LAI) is one of the most common leaf area and canopy structure quantifiers. Direct LAI measurement and determination of canopy characteristics in larger areas is unrealistic due to the large number of measurements required to create the distribution model. This study compares the regression models for the ALS-based calculation of LAI, where the effective leaf area index (eLAI) determined by optical methods and the LAI determined by the direct destructive method and developed by allometric equations were used as response variables. LiDAR metrics and the laser penetration index (LPI) were used as predictor variables. The regression models of LPI and eLAI dependency and the LiDAR metrics and eLAI dependency showed coefficients of determination (R2) of 0.75 and 0.92, respectively; the advantage of using LiDAR metrics for more accurate modelling is demonstrated. The model for true LAI estimation reached a R2 of 0.88.

scholarly journals Correlação linear entre a precipitação e o Índice de Área Foliar do bioma Caatinga

2020 ◽  
Vol 13 (07) ◽  
pp. 3304
Author(s):  
Josiclêda Domiciano Galvíncio ◽  
Sandra Maria Mendes ◽  
Weronica Meira Souza ◽  
Magna Soelma Beserra de Moura ◽  
Wanderson Santos
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Temporal Dynamics ◽  
Correction Method ◽  
Area Index ◽  
Caatinga Biome ◽  
Linear Correction ◽  
Eta Model ◽  
Statistical Relationships

Sabe-se que a precipitação é uma variável de difícil estimativa em especial nas regiões semiáridas. Mesmo com os diversos estudos e avanços já obtidos ainda se necessita desenvolver modelos que possam proporcionar estimativas mais reais. Com o intuito de contribuir nessa linha de pesquisa, este estudo teve como objetivo avaliar a relação existente entre a precipitação e o índice de área foliar no bioma caatinga. Para tanto se faz necessário uma boa distribuição espacial da precipitação uma vez que com o uso do sensoriamento remoto é possível obter uma boa estimativa de índice de área foliar espacialmente. Os dados de precipitação utilizados neste estudo foram obtidos pelo o modelo ETA. Os dados de índice de área foliar foram obtidos pelo o sensor MODIS. Utilizou-se o método de correção linear simples. As relações estatísticas mostraram uma boa correlação entre o índice de área foliar e a precipitação.  Assim, conclui-se que o entendimento da dinâmica do índice de área foliar espacial e temporal pode ajudar no entendimento da dinâmica espacial e temporal da precipitação na caatinga. Acredita-se que a estimativa da precipitação pelo modelo ETA pode ser melhorada com o uso do índice de área foliar.Palavras-chave: sensoriamento remoto, LAI, modelo ETA, precipitação, caatinga. Linear correlation between rainfall and Leaf Area Index of the Caatinga biome A B S T R A C TIt is known that rainfall is a variable that is difficult to estimate, especially in semiarid regions. Even with the various studies and advances already obtained, it is still necessary to develop models that can provide more real estimates. In order to contribute to this line of research, this study aimed to assess the relationship between rainfall and the leaf area index in the caatinga biome. Therefore, a good spatial distribution of precipitation is necessary since with the use of remote sensing it is possible to obtain a good estimate of the spatial leaf area index. The precipitation data used in this study were obtained using the ETA model. The leaf area index data were obtained by the MODIS sensor. The simple linear correction method was used. The statistical relationships showed a good correlation between the leaf area index and precipitation. Thus, it is concluded that understanding the dynamics of the spatial and temporal leaf area index can help in understanding the spatial and temporal dynamics of precipitation in the caatinga. It is believed that the precipitation estimate by the ETA model can be improved with the use of the leaf area index.Keywords: remote sensing, LAI, ETA model, rainfall, caatinga.

Assessing Performance of Leaf Area Index in a Monitored Mountain Ecosystem on Mount Elgon-Uganda

2019 ◽  
pp. 791-808
Author(s):  
Tonny Oyana ◽  
Ellen Kayendeke ◽  
Samuel Adu-Prah
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Global Climate ◽  
Field Measurements ◽  
Area Index ◽  
Temporal Differences ◽  
Mountain Ecosystem ◽  
Mount Elgon

This study investigated the performance of leaf area index (LAI) and photosynthetically active radiation (PAR) in a mountain ecosystem. The authors hypothesized that significant spatial and temporal differences exist in LAI and PAR values in the Manafwa catchment on Mt. Elgon. This was accomplished through field measurements of actual LAI and PAR values of diverse vegetation types along a ~900m altitudinal gradient (1141–2029 masl) in the catchment. In-situ measurements were obtained from 841 micro-scale study plots in 28 sampling plots using high resolution LAI sensors. The findings showed a significant positive relationship exists between elevation and observed LAI (r = 0.45, p = 0.01). A regression model further shows that elevation and curvature of the landscape slope were highly significant (p < 0.00002) predictors of LAI. Finally, the authors detected significant spatial and temporal differences in LAI and PAR values in the study area. The study provides a critical basis for setting up long-term monitoring plans to understand mountain ecosystems and global climate change.

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