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

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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Joint estimation of Plant Area Index (PAI) and wet biomass in wheat and soybean from C-band polarimetric SAR data

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2019.02.007 ◽

2019 ◽

Vol 79 ◽

pp. 24-34 ◽

Cited By ~ 8

Author(s):

Dipankar Mandal ◽

Vineet Kumar ◽

Heather McNairn ◽

Avik Bhattacharya ◽

Y.S. Rao

Keyword(s):

Joint Estimation ◽

Polarimetric Sar ◽

Area Index ◽

Wet Biomass ◽

Plant Area Index ◽

Plant Area ◽

Sar Data

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Microclimate benefits that different street tree species provide to sidewalk pedestrians relate to differences in Plant Area Index

Landscape and Urban Planning ◽

10.1016/j.landurbplan.2016.08.010 ◽

2017 ◽

Vol 157 ◽

pp. 502-511 ◽

Cited By ~ 44

Author(s):

Ruzana Sanusi ◽

Denise Johnstone ◽

Peter May ◽

Stephen J. Livesley

Keyword(s):

Tree Species ◽

Area Index ◽

Street Tree ◽

Plant Area Index ◽

Plant Area

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Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand

Agricultural and Forest Meteorology ◽

10.1016/0168-1923(91)90108-3 ◽

1991 ◽

Vol 56 (1-2) ◽

pp. 129-143 ◽

Cited By ~ 200

Author(s):

J.M. Chen ◽

T.A. Black ◽

R.S. Adams

Keyword(s):

Forest Stand ◽

Hemispherical Photography ◽

Area Index ◽

Plant Area Index ◽

Plant Area

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Comparison of Empirical and Physical Modelling for Estimation of Biochemical and Biophysical Vegetation Properties: Field Scale Analysis across an Arctic Bioclimatic Gradient

Remote Sensing ◽

10.3390/rs12183073 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3073

Author(s):

Blair E. Kennedy ◽

Douglas J. King ◽

Jason Duffe

Keyword(s):

Numerical Optimization ◽

Vegetation Indices ◽

Physical Modelling ◽

Predictive Performance ◽

High Arctic ◽

Canopy Reflectance ◽

Area Index ◽

Empirical Modelling ◽

Plant Area Index ◽

Reflectance Data

To evaluate the potential of multi-angle hyperspectral sensors for monitoring vegetation variables in Arctic environments, empirical and physical modelling using field data was implemented for the retrieval of leaf and canopy chlorophyll content (LCC, CCC) and plant area index (PAI) measured at four sites situated across a bioclimatic gradient in the Western Canadian Arctic. Field reflectance data were acquired with an ASD FieldSpec (305–1075 nm) and used to simulate CHRIS Mode1 spectra (411–997 nm). Multi-angle measurements were taken corresponding to CHRIS view zenith angles (VZA) (−55°, −36°, 0°, +36°, +55°). Empirical modelling compared parametric regression based on vegetation indices (VIs) to non-parametric Gaussian Processes Regression (GPR). In physical modelling, PROSAIL was inverted using numerical optimization and look-up table (LUT) approaches. Cross-validation of the empirical models ranked GPR as best, followed by simple ratio (SR) with optimally selected NIR and red wavelengths, and then ROSAVI using its published wavelengths (mean r2cv = 0.62, 0.58, and 0.54, respectively across all sites, variables, and VZAs). However, the best predictive performance was achieved by SR followed by GPR and ROSAVI (NRMSEcv = 0.12, 0.16, 0.16, respectively). PROSAIL simulated the multi-angle top-of-canopy reflectance well with numerical optimization (r2 = ~0.99, RMSE = 0.004 ± 0.002), but best performing LUT models of LCC, CCC and PAI were poorer than the empirical approaches (mean r2 = 0.48, mean NRMSE = 0.22). PROSAIL performed best at the high Arctic sparsely vegetated site (r2 = 0.57–0.86 for all parameters). Overall, the best performing VZA was −55° for empirical modelling and 0° and ±55° for physical modelling; however, these were not significantly better than the other VZAs. Overall, this study demonstrates that, for Arctic vegetation, nadir narrowband reflectance data used to derive simple empirical VIs with optimally selected bands is a more efficient approach for modelling chlorophyll and PAI than more complex empirical and physical approaches.

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

Tree Physiology ◽

10.1093/treephys/7.1-2-3-4.107 ◽

1990 ◽

Vol 7 (1-2-3-4) ◽

pp. 107-113 ◽

Cited By ~ 18

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

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

Biogeosciences Discussions ◽

10.5194/bgd-9-15633-2012 ◽

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.

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NEW VEGETATION INDICES FOR FULL AND COMPACT POLARIMETRIC SAR DATA: IN PREPARATION FOR THE RADARSAT CONSTELLATION MISSION (RCM)

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-3-w2-2020-41-2020 ◽

2020 ◽

Vol IV-3/W2-2020 ◽

pp. 41-46

Author(s):

D. Ratha ◽

D. Mandal ◽

S. Dey ◽

A. Bhattacharya ◽

A. Frery ◽

...

Keyword(s):

Time Series ◽

Vegetation Index ◽

Time Series Data ◽

Geodesic Distance ◽

Vegetation Indices ◽

Series Data ◽

Biophysical Parameters ◽

Area Index ◽

Plant Area Index ◽

Sar Data

Abstract. In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version uses the ideal depolariser to model the randomness in the vegetation. We have utilized the RADARSAT Constellation Mission (RCM) time-series data from the SAMPVEX16-MB campaign in the Manitoba region of Canada for comparing and assessing the indices in terms of the change in the biophysical parameters as well. The compact-pol data for comparison is simulated from the full-pol RCM time series. Both the indices show better performance at correlating with biophysical parameters such as Plant Area Index (PAI) and Volumetric Water Content (VWC) for wheat and soybean crops, in comparison to the state-of-art Radar Vegetation Index (RVI) of Kim and van Zyl. These indices are timely for the upcoming release of the data from the RCM, which will provide data in both full and compact-pol modes, aimed at better crop monitoring from space.

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