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

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.

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Ammonia emissions from deciduous forest after leaf fall

Biogeosciences ◽

10.5194/bg-10-4577-2013 ◽

2013 ◽

Vol 10 (7) ◽

pp. 4577-4589 ◽

Cited By ~ 18

Author(s):

K. Hansen ◽

L. L. Sørensen ◽

O. Hertel ◽

C. Geels ◽

C. A. Skjøth ◽

...

Keyword(s):

Forest Ecosystems ◽

Deciduous Forest ◽

Temporal Structure ◽

Beech Forest ◽

Deciduous Forests ◽

Leaf Fall ◽

Natural Ecosystems ◽

Area Index ◽

Model Calculations ◽

Plant Area Index

Abstract. The understanding of biochemical feedback 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-day period during autumn 2010 (21 October to 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 of the atmospheric NH3 concentration were obtained with the Danish Ammonia MOdelling System (DAMOS). The relative contribution from the forest components to the atmospheric NH3 flux was assessed using a simple two-layer bi-directional canopy compensation point model. A total of 57.7% of the fluxes measured showed emission and 19.5% showed deposition. A clear tendency of the flux going from deposition of −0.25 ± 0.30 μg NH3-N m−2 s−1 to emission of up to 0.67 ± 0.28 μg NH3-N m−2 s−1 throughout the measurement period was found. In the leaf fall period (23 October to 8 November), an increase in the atmospheric NH3 concentrations was related to the increasing forest NH3 flux. Following leaf fall, the magnitude and temporal structure of the measured NH3 emission fluxes could be adequately reproduced with the bi-directional resistance model; it suggested the forest ground layer (soil and litter) to be the main contributing component to the NH3 emissions. The modelled concentration from DAMOS fits well the measured concentrations before leaf fall, but 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.

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Robust processing of airborne laser scans to plant area density profiles

Biogeosciences ◽

10.5194/bg-17-5939-2020 ◽

2020 ◽

Vol 17 (23) ◽

pp. 5939-5952

Author(s):

Johan Arnqvist ◽

Julia Freier ◽

Ebba Dellwik

Keyword(s):

Beech Forest ◽

Airborne Lidar ◽

Small Scale ◽

Average Intensity ◽

Forest Site ◽

Area Index ◽

Area Density ◽

Intensity Information ◽

Plant Area Index ◽

Plant Area

Abstract. We present a new algorithm for the estimation of the plant area density (PAD) profiles and plant area index (PAI) for forested areas based on data from airborne lidar. The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods do not use the intensity information at all, use only average intensity values, or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new algorithm to three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full-leaf) and winter (bare-tree) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs. Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites. Specific weak points regarding the estimation of the PAD from airborne lidar data are addressed including the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is reduced in the new algorithm, by combining benefits of earlier algorithms. We further show that low-resolution gridding of the PAD will lead to a negative bias in the resulting estimate according to Jensen's inequality for convex functions and that the severity of this bias is method dependent. As a result, the PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for the PAD gridding of airborne lidar scans.

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Robust processing of airborne laser scans to plant area density profiles

10.5194/bg-2020-121 ◽

2020 ◽

Author(s):

Johan Arnqvist ◽

Julia Freier ◽

Ebba Dellwik

Keyword(s):

Beech Forest ◽

Airborne Lidar ◽

Small Scale ◽

Average Intensity ◽

Forest Site ◽

Area Index ◽

Area Density ◽

Intensity Information ◽

Plant Area Index ◽

Plant Area

Abstract. We present a new algorithm for the estimation of plant area density (PAD) proﬁles and plant area index (PAI) for forested areas based on data from airborne lidar. The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods either do not use the intensity information at all, only use average intensity values or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new and three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full leaf) and winter (bare trees) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs. Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites, and further and more importantly – it never produces clearly dubious results. Speciﬁc weak points for estimation of PAD from airborne lidar data are addressed; the inﬂuence of ground reﬂections and the effect of small-scale heterogeneity, and we show how the effect of these points is minimized using the new algorithm. We further show that low-resolution gridding of PAD will lead to a negative bias in the resulting estimate according to Jensen’s inequality for concave functions, and that the severity of this bias is method-dependent. As a result, PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for PAD gridding of airborne lidar scans.

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

Water ◽

10.3390/w13223218 ◽

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.

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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

Forestry Studies / Metsanduslikud Uurimused ◽

10.2478/fsmu-2013-0008 ◽

2013 ◽

Vol 59 (1) ◽

pp. 13-27 ◽

Cited By ~ 2

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.

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

Forests ◽

10.3390/f11050520 ◽

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.

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