scholarly journals Using VEGNET In-Situ Monitoring LiDAR (IML) to Capture Dynamics of Plant Area Index, Structure and Phenology in Aspen Parkland Forests in Alberta, Canada

Forests ◽  
2014 ◽  
Vol 5 (5) ◽  
pp. 1053-1068 ◽  
Author(s):  
Carlos Portillo-Quintero ◽  
Arturo Sanchez-Azofeifa ◽  
Darius Culvenor
Keyword(s):  
In Situ Monitoring ◽  
Index Structure ◽  
Area Index ◽  
Plant Area Index ◽  
Aspen Parkland ◽  
Plant Area

scholarly journals Electromagnetic Induction Is a Fast and Non-Destructive Approach to Estimate the Influence of Subsurface Heterogeneity on Forest Canopy Structure

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3218
Author(s):  
Simon Damien Carrière ◽  
Nicolas K. Martin-StPaul ◽  
Claude Doussan ◽  
François Courbet ◽  
Hendrik Davi ◽  
...  
Keyword(s):  
Forest Structure ◽  
Electromagnetic Induction ◽  
Forest Canopy ◽  
Soil Depth ◽  
Soil Conditions ◽  
Mediterranean Forests ◽  
Area Index ◽  
Edaphic Conditions ◽  
Plant Area Index ◽  
Plant Area

The spatial forest structure that drives the functioning of these ecosystems and their response to global change is closely linked to edaphic conditions. However, the latter properties are particularly difficult to characterize in forest areas developed on karst, where soil is highly rocky and heterogeneous. In this work, we investigated whether geophysics, and more specifically electromagnetic induction (EMI), can provide a better understanding of forest structure. We use EMI (EM31, Geonics Limited, Ontario, Canada) to study the spatial variability of ground properties in two different Mediterranean forests. A naturally post-fire regenerated forest composed of Aleppo pines and Holm oaks and a monospecific plantation of Altlas cedar. To better interpret EMI results, we used electrical resistivity tomography (ERT), soil depth surveys, and field observations. Vegetation was also characterized using hemispherical photographs that allowed to calculate plant area index (PAI). Our results show that the variability of ground properties contribute to explaining the variability in the vegetation cover development (plant area index). Vegetation density is higher in areas where the soil is deeper. We showed a significant correlation between edaphic conditions and tree development in the naturally regenerated forest, but this relationship is clearly weaker in the cedar plantation. We hypothesized that regular planting after subsoiling, as well as sylvicultural practices (thinning and pruning) influenced the expected relationship between vegetation structure and soil conditions measured by EMI. This work opens up new research avenues to better understand the interplay between soil and subsoil variability and forest response to climate change.

scholarly journals Restoration of above canopy reference hemispherical image from below canopy measurements for plant area index estimation in forests/ Metsa võrastiku läbipaistvuse mõõtmine digitaalsete poolsfäärikaamerate abil

2013 ◽  
Vol 59 (1) ◽  
pp. 13-27 ◽  
Author(s):  
Mait Lang ◽  
Ave Kodar ◽  
Tauri Arumäe
Keyword(s):  
Field Measurements ◽  
Incident Radiation ◽  
Canopy Gap ◽  
Raw Data ◽  
Area Index ◽  
Gap Fraction ◽  
Cmos Sensor ◽  
Plant Area Index ◽  
Plant Area ◽  
Thresholding Algorithm

Abstract Canopy gap fraction has been estimated from hemispherical images using a thresholding method to separate sky and canopy pixels. The optimal objective thresholding rule has been searched by many authors without satisfactory results due to long list of reasons. Some recent studies have shown that unprocessed readings of camera CCD or CMOS sensor (raw data) have linear relationship with incident radiation. This allows a pair of cameras used in similar to a pair of plant canopy analyzers and canopy gap fraction can be calculated as the ratio of below canopy image and above canopy image. We tested new freeware program HemiSpherical Project Manager (HSP) for the restoration of the above canopy image from below canopy image which allows making field measurements with single below canopy operated camera. Results of perforated panel image analysis and comparison of plant area index (PAI) estimated independently by three operators from real canopy hemispherical images showed high degree of reliability of the new approach. Determination coefficients of linear regression of the PAI estimations of the three operators were 0.9962, 0.9875 and 0.9825. The canopy gap fraction data obtained from HSP were used to validate Nobis-Hunziker automatic thresholding algorithm. The results indicated that the Nobis-Hunziker algorithm underestimated PAI from out of camera JPEG images and overestimated PAI from raw data.

scholarly journals Applying LiDAR to Quantify the Plant Area Index Along a Successional Gradient in a Tropical Forest of Thailand

Forests ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 520
Author(s):  
Siriruk Pimmasarn ◽  
Nitin Kumar Tripathi ◽  
Sarawut Ninsawat ◽  
Nophea Sasaki
Keyword(s):  
National Park ◽  
Forest Succession ◽  
Secondary Forests ◽  
Area Index ◽  
Khao Yai National Park ◽  
Plant Area Index ◽  
Plant Area ◽  
Successional Process ◽  
Term Monitoring

Long-term monitoring of vegetation is critical for understanding the dynamics of forest ecosystems, especially in Southeast Asia’s tropical forests, which play a significant role in the global carbon cycle and have continually been converted into various stages of secondary forests. In Thailand, long-term monitoring of forest dynamics during the successional process is limited to plot scales assuming from the distinct structure of successional stages. Our study highlights the potential of coupling airborne light detection and ranging (LiDAR) technology and stand age data derived from Landsat time-series to track back forest succession, and infer patterns in the plant area index (PAI) recovery. Here, using LIDAR data, we estimated the PAI of the 510 sample plots of a seasonal evergreen forest dispersed over the study area in Khao Yai National Park, Thailand, capturing a successional gradient of tropical secondary forests. The sample plots age was derived from the available Landsat time-series dataset (1972–2017). We developed a PAI recovery model during the first 42 years of the succession process. We investigated the relationship between the model residuals and PAI values with topographic factors, such as elevation, slope, and topographic wetness index. The results show that the PAI increased non-linearly (pseudo-R2 of 0.56) during the first 42 years of forest succession, and all three topographic factors have less influence on PAI variability. These results provide valuable information of the spatio-temporal PAI patterns during the successional process and help understand the dynamics of tropical secondary forests in Khao Yai National Park, Thailand. Such information is essential for forest management and local, regional, and global PAI synthesis. Moreover, our results provide significant information for ground-based spatial sampling strategies to enable more accurate PAI measurements.

Plant area index inEucalyptus globulusplantations determined indirectly by a light interception method

1990 ◽  
Vol 7 (1-2-3-4) ◽  
pp. 107-113 ◽  
Author(s):  
L. C. Gazarini ◽  
M. C. C. Araújo ◽  
N. Borralho ◽  
J. S. Pereira
Keyword(s):  
Light Interception ◽  
Area Index ◽  
Plant Area Index ◽  
Plant Area

scholarly journals Local Illumination Influence on Vegetation Indices and Plant Area Index (PAI) Relationships

2014 ◽  
Vol 6 (7) ◽  
pp. 6266-6282 ◽  
Author(s):  
Flávio Ponzoni ◽  
Clayton Silva ◽  
Sandra Santos ◽  
Otávio Montanher ◽  
Thiago Santos
Keyword(s):  
Vegetation Indices ◽  
Area Index ◽  
Plant Area Index ◽  
Plant Area ◽  
Local Illumination

scholarly journals Ammonia emissions from beech forest after leaf fall – measurements and modelling

2012 ◽  
Vol 9 (11) ◽  
pp. 15633-15665
Author(s):  
K. Hansen ◽  
L. L. Sørensen ◽  
O. Hertel ◽  
C. Geels ◽  
C. A. Skjøth ◽  
...  
Keyword(s):  
Forest Ecosystems ◽  
Beech Forest ◽  
Deciduous Forests ◽  
Leaf Fall ◽  
Natural Ecosystems ◽  
Area Index ◽  
Model Calculations ◽  
Plant Area Index ◽  
Plant Area ◽  
Dynamic Description

Abstract. The understanding of biochemical feed-back mechanisms in the climate system is lacking knowledge in relation to bi-directional ammonia (NH3) exchange between natural ecosystems and the atmosphere. We therefore study the atmospheric NH3 fluxes during a 25 days period during autumn 2010 (21 October–15 November) for the Danish beech forest, Lille Bøgeskov, to address the hypothesis that NH3 emissions occur from deciduous forests in relation to leaf fall. This is accomplished by using observations of vegetation status, NH3 fluxes and model calculations. Vegetation status was observed using plant area index (PAI) and leaf area index (LAI). NH3 fluxes were measured using the relaxed eddy accumulation (REA) method. The REA based NH3 concentrations were compared to NH3 denuder measurements. Model calculations were obtained with the Danish Ammonia MOdelling System (DAMOS). 57.7% of the fluxes measured showed emission and 19.5% showed deposition. The mean NH3 flux was 0.087 ± 0.19 μg NH3-N m−2 s−1. A clear tendency of the flux going from negative (deposition) to positive (emission) fluxes of up to 0.96 ± 0.40 μg NH3-N m−2 s−1 throughout the measurement period was found. In the leaf fall period (23 October–8 November), an increase in the atmospheric NH3 concentrations was related to the increasing forest NH3 flux. The modelled concentration from DAMOS fits well the measured concentrations before leaf fall. During and after leaf fall, the modelled concentrations are too low. The results indicate that the missing contribution to atmospheric NH3 concentration from vegetative surfaces related to leaf fall are of a relatively large magnitude. We therefore conclude that emissions from deciduous forests are important to include in model calculations of atmospheric NH3 for forest ecosystems. Finally, diurnal variations in the measured NH3 concentrations were related to meteorological conditions, forest phenology and the spatial distribution of local anthropogenic NH3 sources. This suggests that an accurate description of ammonia fluxes over forest ecosystems requires a dynamic description of atmospheric and vegetation processes.

scholarly journals The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

2020 ◽  
Vol 12 (12) ◽  
pp. 1979
Author(s):  
Dandan Xu ◽  
Deshuai An ◽  
Xulin Guo
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Area Index ◽  
Plant Area Index ◽  
The Impact

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

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