scholarly journals Evolución del comportamiento espectral y la composición química en el dosel arbóreo de una dehesa

2016 ◽  
pp. 31
Author(s):  
R. González-Cascón ◽  
J. Pacheco-Labrador ◽  
M. P. Martín
Keyword(s):  
Forest Canopy ◽  
Chlorophyll Concentration ◽  
Visible Region ◽  
Tree Canopy ◽  
Leaf Water Content ◽  
Least Squares Regression ◽  
Nutrient Contents ◽  
Spectral Bands ◽  
Reflectance Factor ◽  
Leaf Level

<p>In the context of the BIOSPEC and FLUXPEC projects (http://www.lineas.cchs.csic.es/fluxpec/), spectral and biophysical variables measurements at leaf level have been conducted in the tree canopy of a holm oak dehesa (Quercus ilex) ecosystem during four vegetative periods. Measurements of bi-conical reflectance factor of intact leaf (ASD Fieldspec 3® spectroradiometer), specific leaf mass (SLM), leaf water content (LWC), nutrient (N, P, K, Ca, Mg, Mn, Fe, and Zn) and chlorophyll concentration were performed. The spectral measurements have been related with the biophysical variables by stepwise and partial least squares regression analyses. These analyses allowed to identify the spectral bands and regions that best explain the evolution of the biophysical variables and to estimate the nutrient contents during the leaf maturation process. Statistically significant estimates of the majority of the variables studied were obtained. Wavelengths that had the highest contributions explaining the chemical composition of the forest canopy were located in spectral regions of the red edge, the green visible region, and the shortwave infrared.</p>

scholarly journals Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

2020 ◽  
Vol 12 (13) ◽  
pp. 2082 ◽  
Author(s):  
Sourav Bhadra ◽  
Vasit Sagan ◽  
Maitiniyazi Maimaitijiang ◽  
Matthew Maimaitiyiming ◽  
Maria Newcomb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Least Squares ◽  
Chlorophyll Concentration ◽  
Feature Space ◽  
Hyperspectral Data ◽  
Leaf Chlorophyll ◽  
Reflectance Factor ◽  
Leaf Level ◽  
Leaf Chlorophyll Concentration

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.

scholarly journals Hypertemporal Imaging Capability of UAS Improves Photogrammetric Tree Canopy Models

2020 ◽  
Vol 12 (8) ◽  
pp. 1238 ◽  
Author(s):  
Andrew Fletcher ◽  
Richard Mather
Keyword(s):  
Forest Canopy ◽  
Near Infrared ◽  
Spectral Band ◽  
Point Clouds ◽  
Tree Canopy ◽  
Solar Elevation ◽  
Spectral Bands ◽  
Eucalyptus Forest ◽  
Dense Point ◽  
Solar Noon

Small uncrewed aerial systems (UASs) generate imagery that can provide detailed information regarding condition and change if the products are reproducible through time. Densified point clouds form the basic information for digital surface models and orthorectified mosaics, so variable dense point reconstruction will introduce uncertainty. Eucalyptus trees typically have sparse and discontinuous canopies with pendulous leaves that present a difficult target for photogrammetry software. We examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy. At the study site near solar noon, selecting near infrared camera increased projected tree canopy fourfold, and dense point features more than 2 m above ground were increased sixfold compared to red spectral bands. Near infrared (NIR) imagery improved projected and total dense features two- and threefold, respectively, compared to default green band imagery. The lowest solar elevation captured (25°) consistently improved canopy feature reconstruction in all spectral bands. Although low solar elevations are typically avoided for radiometric reasons, we demonstrate that these conditions improve the detection and reconstruction of complex tree canopy features in natural Eucalyptus forests. Combining imagery sets captured at different solar elevations improved the reproducibility of dense point clouds between seasons. Total dense point cloud features reconstructed were increased by almost 10 million points (20%) when imagery used was NIR combining solar noon and low solar elevation imagery. It is possible to use agricultural multispectral camera rigs to reconstruct Eucalyptus tree canopy and shrub swamp by combining imagery and selecting appropriate spectral bands for processing.

scholarly journals Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Google Earth ◽  
Tree Canopy ◽  
Landsat Data ◽  
High Resolution Data ◽  
Modis Ndvi ◽  
Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

scholarly journals Modelling bi-directional fluxes of methanol and acetaldehyde with the FORCAsT canopy exchange model

2016 ◽  
Author(s):  
Kirsti Ashworth ◽  
Serena H. Chung ◽  
Karena A. McKinney ◽  
Ying Liu ◽  
Bill J. Munger ◽  
...  
Keyword(s):  
Stomatal Conductance ◽  
Atmospheric Chemistry ◽  
Forest Canopy ◽  
Global Scale ◽  
Exchange Model ◽  
Forest Site ◽  
Canopy Exchange ◽  
Harvard Forest ◽  
Leaf Level ◽  
Underlying Mechanisms

Abstract. The FORCAsT canopy exchange model was used to investigate the underlying mechanisms governing foliage emissions of methanol and acetaldehyde, two short chain oxygenated volatile organic compounds ubiquitous in the troposphere and known to have strong biogenic sources, at a northern mid-latitude forest site. The explicit representation of the vegetation canopy within the model allowed us to test the hypothesis that stomatal conductance regulates emissions of these compounds to an extent that its influence is observable at the ecosystem-scale, a process not currently considered in regional or global scale atmospheric chemistry models. We found that FORCAsT could only reproduce the magnitude and diurnal profiles of methanol and acetaldehyde fluxes measured at the top of the forest canopy at Harvard Forest if light-dependent emissions were introduced to the model. With the inclusion of such emissions FORCAsT was able to successfully simulate the observed bi-directional exchange of methanol and acetaldehyde. Although we found evidence that stomatal conductance influences methanol fluxes and concentrations at scales beyond the leaf-level, particularly at dawn and dusk, we were able to adequately capture ecosystem exchange without the addition of stomatal control to the standard parameterisations of foliage emissions, suggesting that ecosystem fluxes can be well enough represented by the emissions models currently used.

scholarly journals Evaluation of the throughfall and stemflow nutrient contents in mixed and pure plantations of Acacia mangium, Pseudosamenea guachapele and Eucalyptus grandis

2007 ◽  
Vol 31 (2) ◽  
pp. 339-346 ◽  
Author(s):  
Fabiano de Carvalho Balieiro ◽  
Avílio Antônio Franco ◽  
Renildes Lúcio Ferreira Fontes ◽  
Luiz Eduardo Dias ◽  
Eduardo Francia Carneiro Campello ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Eucalyptus Grandis ◽  
Acacia Mangium ◽  
Tropical Soils ◽  
Low Fertility ◽  
Pure Stand ◽  
Nitrogen Fixing ◽  
Nutrient Input ◽  
Nutrient Contents ◽  
Mixed Plantations

The interception of the rainfall by the forest canopy has great relevance to the nutrient geochemistry cycle in low fertility tropical soils under native or cultivated forests. However, little is known about the modification of the rainfall water quality and hydrological balance after interception by the canopies of eucalyptus under pure and mixed plantations with leguminous species, in Brazil. Samples of rainfall (RF), throughfall (TF) and stemflow (SF) were collected and analyzed in pure plantations of mangium (nitrogen fixing tree -NFT), guachapele (NFT) and eucalyptus (non-nitrogen fixing tree -NNFT) and in a mixed stand of guachapele and eucalyptus in Seropédica, State of Rio de Janeiro, Brazil. Nine stemflow collectors (in selected trees) and nine pluviometers were randomly disposed under each stand and three pluviometers were used to measure the incident rainfall during 5.5 months. Mangium conveyed 33.4% of the total rainfall for its stem. An estimative based on corrections for the average annual precipitation (1213 mm) indicated that the rainfall's contribution to the nutrient input (kg ha-1) was about 8.42; 0.95; 19.04; 6.74; 4.72 and 8.71 kg ha-1 of N-NH4+, P, K+, Ca+2, Mg+2 and Na+, respectively. Throughfall provided the largest contributions compared to the stemflow nutrient input. The largest inputs of N-NH4+ (15.03 kg ha-1) and K+ (179.43 kg ha-1) were observed under the guachapele crown. Large amounts of Na+ denote a high influence of the sea. Mangium was the most adapted species to water competitiveness. Comparatively to pure stand of eucalyptus, the mixed plantation intensifies the N, Ca and Mg leaching by the canopy, while the inputs of K and P were lower under these plantations.

scholarly journals Technical Note: Multispectral lidar time series of pine canopy chlorophyll content

2015 ◽  
Vol 12 (5) ◽  
pp. 1629-1634 ◽  
Author(s):  
T. Hakala ◽  
O. Nevalainen ◽  
S. Kaasalainen ◽  
R. Mäkipää
Keyword(s):  
Chlorophyll A ◽  
Point Cloud ◽  
Laser Scanning ◽  
Technical Note ◽  
Tree Canopy ◽  
Tree Level ◽  
Spatial Changes ◽  
New Instrument ◽  
Leaf Level ◽  
Chlorophyll A And B

Abstract. We present an empirical application of multispectral laser scanning for monitoring the seasonal and spatial changes in pine chlorophyll (a + b) content and upscaling the accurate leaf-level chlorophyll measurements into branch and tree level. The results show the capability of the new instrument for monitoring the changes in the shape and physiology of tree canopy: the spectral indices retrieved from the multispectral point cloud agree with laboratory measurements of the chlorophyll a and b content. The approach opens new prospects for replacing destructive and labour-intensive manual sampling with remote observations of tree physiology.

scholarly journals Natural Regeneration of the Tree Stand in the Bilberry Spruce Forest—Clear-Cutting Ecotone Complex in the First Post-Logging Decade

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1542
Author(s):  
Nadezhda V. Genikova ◽  
Viktor N. Mamontov ◽  
Alexander M. Kryshen ◽  
Vladimir A. Kharitonov ◽  
Sergey A. Moshnikov ◽  
...  
Keyword(s):  
Natural Regeneration ◽  
Forest Canopy ◽  
Forest Cover ◽  
Forest Type ◽  
Ground Cover ◽  
Woody Species ◽  
Forest Edge ◽  
Tree Canopy ◽  
Clear Cut ◽  
Clear Cutting

Bilberry spruce forests are the most widespread forest type in the European boreal zone. Limiting the clear-cuttings size leads to fragmentation of forest cover and the appearance of large areas of ecotone complexes, composed of forest (F), a transition from forest to the cut-over site under tree canopy (FE), a transition from forest to the cut-over site beyond tree canopy (CE), and the actual clear-cut site (C). Natural regeneration of woody species (spruce, birch, rowan) in the bilberry spruce stand—clear-cut ecotone complex was studied during the first decade after logging. The effects produced by the time since cutting, forest edge aspect, and the ground cover on the emergence and growth of trees and shrubs under forest canopy and openly in the clear-cut were investigated. Estimating the amount and size of different species in the regeneration showed FE and CE width to be 8 m—roughly half the height of first-story trees. Typical forest conditions (F) feature a relatively small amount of regenerating spruce and birch. The most favorable conditions for natural regeneration of spruce in the clear-cut—mature bilberry spruce stand ecotone are at the forest edge in areas of transition both towards the forest and towards the clear-cut (FE and CE). Clear-cut areas farther from the forest edge (C) offer an advantage to regenerating birch, which grows densely and actively in this area.

scholarly journals ESTIMATION OF REGIONAL FOREST ABOVEGROUND BIOMASS COMBINING ICESAT-GLAS WAVEFORMS AND HJ-1A/HSI HYPERSPECTRAL IMAGERIES

2016 ◽  
Vol XLI-B7 ◽  
pp. 731-737
Author(s):  
Yanqiu Xing ◽  
Sai Qiu ◽  
Jianhua Ding ◽  
Jing Tian
Keyword(s):  
Carbon Cycle ◽  
Aboveground Biomass ◽  
Forest Canopy ◽  
Field Investigation ◽  
Support Vector ◽  
Laser Altimeter ◽  
Spectral Bands ◽  
Spectral Imager ◽  
Regional Forest ◽  
Forest Aboveground Biomass

Estimation of forest aboveground biomass (AGB) is a critical challenge for understanding the global carbon cycle because it dominates the dynamics of the terrestrial carbon cycle. Light Detection and Ranging (LiDAR) system has a unique capability for estimating accurately forest canopy height, which has a direct relationship and can provide better understanding to the forest AGB. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) is the first polarorbiting LiDAR instrument for global observations of Earth, and it has been widely used for extracting forest AGB with footprints of nominally 70&thinsp;m in diameter on the earth's surface. However, the GLAS footprints are discrete geographically, and thus it has been restricted to produce the regional full coverage of forest AGB. To overcome the limit of discontinuity, the Hyper Spectral Imager (HSI) of HJ-1A with 115 bands was combined with GLAS waveforms to predict the regional forest AGB in the study. Corresponding with the field investigation in Wangqing of Changbai Mountain, China, the GLAS waveform metrics were derived and employed to establish the AGB model, which was used further for estimating the AGB within GLAS footprints. For HSI imagery, the Minimum Noise Fraction (MNF) method was used to decrease noise and reduce the dimensionality of spectral bands, and consequently the first three of MNF were able to offer almost 98% spectral information and qualified to regress with the GLAS estimated AGB. Afterwards, the support vector regression (SVR) method was employed in the study to establish the relationship between GLAS estimated AGB and three of HSI MNF (i.e. <i>MNF1</i>, <i>MNF2</i> and <i>MNF3</i>), and accordingly the full covered regional forest AGB map was produced. The results showed that the adj.R<sup>2</sup> and RMSE of SVR-AGB models were 0.75 and 4.68&thinsp;t&thinsp;hm<sup>&minus;2</sup> for broadleaf forests, 0.73 and 5.39&thinsp;t&thinsp;hm<sup>&minus;2</sup> for coniferous forests and 0.71 and 6.15&thinsp;t&thinsp;hm<sup>&minus;2</sup> for mixed forests respectively. The full covered regional forest AGB map of the study area had 0.62 of accuracy and 11.11&thinsp;t&thinsp;hm<sup>&minus;2</sup> of RMSE. The study demonstrated that it holds great potential to achieve the full covered regional forest AGB distribution with higher accuracy by combing LiDAR data and hyperspectral imageries.

scholarly journals Low sensitivity of gross primary production to elevated CO<sub>2</sub> in a mature eucalypt woodland

2020 ◽  
Vol 17 (2) ◽  
pp. 265-279 ◽  
Author(s):  
Jinyan Yang ◽  
Belinda E. Medlyn ◽  
Martin G. De Kauwe ◽  
Remko A. Duursma ◽  
Mingkai Jiang ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Forest Canopy ◽  
Gross Primary Production ◽  
Carbon Dioxide Concentration ◽  
Co2 Enrichment ◽  
Photosynthetic Acclimation ◽  
Leaf Photosynthesis ◽  
Ambient Conditions ◽  
Area Index ◽  
Leaf Level

Abstract. The response of mature forest ecosystems to a rising atmospheric carbon dioxide concentration (Ca) is a major uncertainty in projecting the future trajectory of the Earth's climate. Although leaf-level net photosynthesis is typically stimulated by exposure to elevated Ca (eCa), it is unclear how this stimulation translates into carbon cycle responses at the ecosystem scale. Here we estimate a key component of the carbon cycle, the gross primary productivity (GPP), of a mature native eucalypt forest exposed to free-air CO2 enrichment (the EucFACE experiment). In this experiment, light-saturated leaf photosynthesis increased by 19 % in response to a 38 % increase in Ca. We used the process-based forest canopy model, MAESPA, to upscale these leaf-level measurements of photosynthesis with canopy structure to estimate the GPP and its response to eCa. We assessed the direct impact of eCa, as well as the indirect effect of photosynthetic acclimation to eCa and variability among treatment plots using different model scenarios. At the canopy scale, MAESPA estimated a GPP of 1574 g C m−2 yr−1 under ambient conditions across 4 years and a direct increase in the GPP of +11 % in response to eCa. The smaller canopy-scale response simulated by the model, as compared with the leaf-level response, could be attributed to the prevalence of RuBP regeneration limitation of leaf photosynthesis within the canopy. Photosynthetic acclimation reduced this estimated response to 10 %. After taking the baseline variability in the leaf area index across plots in account, we estimated a field GPP response to eCa of 6 % with a 95 % confidence interval (−2 %, 14 %). These findings highlight that the GPP response of mature forests to eCa is likely to be considerably lower than the response of light-saturated leaf photosynthesis. Our results provide an important context for interpreting the eCa responses of other components of the ecosystem carbon cycle.

scholarly journals Measurements of NO and NO<sub>2</sub> exchange between the atmosphere and <i>Quercus agrifolia</i>

2018 ◽  
Author(s):  
Erin R. Delaria ◽  
Megan Vieira ◽  
Julie Cremieux ◽  
Ronald C. Cohen
Keyword(s):  
Forest Canopy ◽  
Field Measurements ◽  
Atmospheric Model ◽  
Ambient Conditions ◽  
Quercus Agrifolia ◽  
Deposition Velocities ◽  
Uptake Rates ◽  
Leaf Level ◽  
Oak Tree ◽  
Chamber Studies

Abstract. NO2 foliar deposition through the stomata of leaves has been identified as a significant sink of NOx within a forest canopy. In this study, we investigated NO2 and NO exchange between the atmosphere and the leaves of the native California oak tree Quercus agrifolia using a branch enclosure system. NO2 detection was performed with laser-induced fluorescence (LIF), which excludes biases from other reactive nitrogen compounds and has a low detection limit of 5–50 ppt. We performed both light and dark experiments with concentrations between 0.5–10 ppb NO2 and NO under constant ambient conditions. Deposition velocities for NO2 during light and dark experiments were 0.123 ± 0.007 cm s−1 and 0.015 ± 0.001 cm s−1, respectively. Much slower deposition was seen for NO, with deposition velocities of 0.012 ± 0.002 cm s−1 and 0.005 ± 0.002 cm s−1 measured during light and dark experiments, respectively. This corresponded to a summed resistance of the stomata and mesophyll of 6.9 ± 0.9 cm s−1 for NO2 and 140 ± 40 cm s−1 for NO. No significant compensation point was detected for NO2 uptake, but compensation points ranging from 0.74–3.8 ppb were observed for NO. NO2 and NO deposition velocities reported here are comparable both with previous leaf-level chamber studies and inferences from canopy-level field measurements. In parallel with these laboratory experiments, we have constructed a detailed 1-D atmospheric model to assess the contribution of leaf-level NOx deposition to the total NOx loss and NOx canopy fluxes. Using the leaf uptake rates measured in the laboratory, these modeling studies suggest loss of NOx to deposition in a California oak woodland competes with the pathways of HNO3 and RONO2 formation, with deposition making up 3–22 % of the total NOx loss. Additionally, foliar uptake of NOx at these rates could account for ~15–30 % canopy reduction of soil NOx emissions.

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