scholarly journals Laboratory and image spectroscopy for evaluating the biophysical state of meadow vegetation in the Krkonoše National Park

2014 ◽  
Vol 18 (2) ◽  
pp. 15-22 ◽  
Author(s):  
Jan Jelének ◽  
Lucie Kupková ◽  
Bogdan Zagajewski ◽  
Stanislav Březina ◽  
Adrian Ochytra ◽  
...  
Keyword(s):  
Invasive Species ◽  
National Park ◽  
Vegetation Indices ◽  
In Situ Measurements ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Area Index ◽  
Area Of Interest ◽  
Maximal Sensitivity

Abstract The paper deals with the evaluation of mountain meadow vegetation condition using in-situ measurements of the fraction of Accumulated Photosynthetically Active Radiation (fAPAR) and Leaf Area Index (LAI). The study analyses the relationship between these parameters and spectral properties of meadow vegetation and selected invasive species with the goal of finding out vegetation indices for the detection of fAPAR and LAI. The developed vegetation indices were applied on hyperspectral data from an APEX (Airborne Prism Experiment) sensor in the area of interest in the Krkonoše National Park. The results of index development on the level of the field data were quite good. The maximal sensitivity expressed by the coefficient of determination for LAI was R2 = 0.56 and R2 = 0.79 for fAPAR. However, the sensitivity of all the indices developed at the image level was quite low. The output values of in-situ measurements confirmed the condition of invasive species as better than that of the valuable original meadow vegetation, which is a serious problem for national park management.

scholarly journals Multiscale observations of NH<sub>3</sub> around Toronto, Canada

2021 ◽  
Vol 14 (2) ◽  
pp. 905-921
Author(s):  
Shoma Yamanouchi ◽  
Camille Viatte ◽  
Kimberly Strong ◽  
Erik Lutsch ◽  
Dylan B. A. Jones ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Grid Point ◽  
Regional Scale ◽  
Model Performance ◽  
In Situ Measurements ◽  
Coefficient Of Determination ◽  
Spatial And Temporal Variability ◽  
Predictive Capacity ◽  
Total Column

Abstract. Ammonia (NH3) is a major source of nitrates in the atmosphere and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 derived from multiscale datasets are examined. These NH3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, 3 years of surface in situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the representativeness of the FTIR measurements. All three time series showed positive trends in NH3 over Toronto: 3.34 ± 0.89 %/yr from 2002 to 2018 in the FTIR columns, 8.88 ± 5.08 %/yr from 2013 to 2017 in the surface in situ data, and 8.38 ± 1.54 %/yr from 2008 to 2018 in the IASI columns. To assess the representative scale of the FTIR NH3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤25 km and ≤20 min, with r=0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in situ measurements were standardized and correlated. Comparison of 24 d averages and monthly averages resulted in correlation coefficients of r=0.72 and r=0.75, respectively, although correlation without averaging to reduce high-frequency variability led to a poorer correlation, with r=0.39. The GEOS-Chem model, run at 2∘ × 2.5∘ resolution, was compared to FTIR and IASI to assess model performance and investigate the correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35 to 53∘ N and 93.75 to 63.75∘ W) resulted in r=0.57 and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r2=0.33, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r2=0.13, indicating that a finer spatial resolution is needed for modeling NH3.

scholarly journals Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6732
Author(s):  
Haixia Qi ◽  
Bingyu Zhu ◽  
Zeyu Wu ◽  
Yu Liang ◽  
Jianwen Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Field Experiments ◽  
Predictive Accuracy ◽  
Vegetation Indices ◽  
Planting Density ◽  
Coefficient Of Determination ◽  
Multispectral Images ◽  
Area Index

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

scholarly journals Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology

2020 ◽  
Vol 12 (19) ◽  
pp. 3216 ◽  
Author(s):  
Matthew Maimaitiyiming ◽  
Vasit Sagan ◽  
Paheding Sidike ◽  
Maitiniyazi Maimaitijiang ◽  
Allison J. Miller ◽  
...  
Keyword(s):  
Canopy Temperature ◽  
Crop Productivity ◽  
In Situ Measurements ◽  
Sensor Technology ◽  
Stress Index ◽  
Coefficient Of Determination ◽  
Neural Net ◽  
Water Stress Index ◽  
Very High

Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs.

scholarly journals Fluxo de CO2 e os Índices de Vegetação do Parque Nacional das Nascentes do Rio Parnaíba, Piauí, Brasil

2020 ◽  
Vol 13 (07) ◽  
pp. 3585
Author(s):  
Luana De Castro Pereira ◽  
Arnon Batista Nunes ◽  
Israel Lobato Rocha ◽  
Janeil Lustosa De Oliveira ◽  
Maria Letícia Stefany Monteiro Brandão ◽  
...  
Keyword(s):  
Carbon Balance ◽  
National Park ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Carbon Flow ◽  
Natural Environments ◽  
Area Index ◽  
Vegetation Indexes ◽  
Co2 Flow ◽  
Good Coverage

As emissões dos gases de efeito estufa na atmosfera trazem consequências para o meio ambiente e saúde pública. Logo, ambientes naturais, como as Florestas Nativas do Cerrado são essenciais no processo de equilíbrio de carbono, pela fixação do mesmo. Com o objetivo estimar o fluxo de CO2 com base em diferentes índices de vegetação do Parque Nacional das Nascentes do Rio Parnaíba (PNNRP), essa pesquisa, utilizou-se dos seguintes índices: Pré Processamento das Imagens (PPI), Índice de Vegetação por Diferença Normalizada – NDVI, Índice de Vegetação Fotossintético – PRI, Índice de Vegetação Ajustado ao Solo – SAVI, Índice de Área Foliar- IAF e CO2FLUX.  Referente ao Índice de Vegetação por Diferença Normalizada (NDVI), verificou-se que a maior parte da área PNNRP se encontra sob a vegetação considerada densa, sendo os  valores de SAVI encontrados próximos aos valores de NDVI, que pode estar relacionado a uma boa cobertura vegetal presente, indicando pouca influência das características do solo sob os índices de vegetação. A partir dos resultados encontrados através do IAF do PNNRP verificou que em áreas que os valores são maiores encontram-se as vegetações com o melhor desenvolvimento. Levando em conta os valores relacionados ao CO2Flux, IAF, NDVI e os demais índices, percebeu-se a capacidade do Parque no aproveitamento da luz solar e a realização da fotossíntese, além de abrigar uma vegetação saudável, podendo assim afirmar o grande potencial do PNNRP em armazenar carbono. Portanto, evidencia-se que o Parque Nacional das Nascentes do Rio Parnaíba possuí uma alto potencial de fluxo de carbono.   CO2 flow and vegetation indices of the Parque Nacional das Nascentes do Rio Parnaíba, Piauí, Brazil A B S T R A C TEmissions of greenhouse gases into the atmosphere have consequences for the environment and public health. Therefore, natural environments, such as the Cerrado's Native Forests are essential in the carbon balance process, due to its fixation. With the objective of estimating the CO2 flow based on different vegetation indexes of the Nascentes do Rio Parnaíba National Park (PNNRP), this research used the following indexes: Pre-Processing of Images (PPI), Vegetation Index by Difference Normalized - NDVI, Photosynthetic Vegetation Index - PRI, Soil Adjusted Vegetation Index - SAVI, Leaf Area Index - IAF and CO2FLUX. Regarding the Index of Vegetation by Normalized Difference (NDVI), it was found that most of the PNNRP area is under dense vegetation, with SAVI values found close to NDVI values, which may be related to good coverage present, indicating little influence of soil characteristics on vegetation indexes. From the results found through the IAF of the PNNRP verified that in areas with higher values are the vegetation with the best development. Taking into account the values related to CO2Flux, IAF, NDVI and other indexes, the Park's capacity to use sunlight and photosynthesis was observed, as well as to house healthy vegetation, thus confirming the great potential of PNNRP in storing carbon. Therefore, it is evident that the Parnaíba River National Park has a high carbon flow potential.Keywords: biomass, cerrado biome, carbon flow

scholarly journals Multiscale observations of NH3 around Toronto, Canada

2020 ◽  
Author(s):  
Shoma Yamanouchi ◽  
Camille Viatte ◽  
Kimberly Strong ◽  
Dylan B. A. Jones ◽  
Cathy Clerbaux ◽  
...  
Keyword(s):  
Case Studies ◽  
Biomass Burning ◽  
Grid Point ◽  
Fine Particulate Matter ◽  
Regional Scale ◽  
Model Performance ◽  
In Situ Measurements ◽  
Coefficient Of Determination ◽  
Predictive Capacity

&lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Ammonia (NH&lt;sub&gt;3&lt;/sub&gt;) is a major source of nitrates in the atmosphere, and a major source of fine particulate matter. As such, there have been increasing efforts to monitor NH&lt;sub&gt;3&lt;/sub&gt;. This study examines long-term measurements of NH&lt;sub&gt;3&lt;/sub&gt; around Toronto, Canada, derived from three multiscale datasets: 16 years of total column measurements using ground-based Fourier transform infrared (FTIR) spectroscopy, three years of surface in-situ measurements, and ten years of total columns from the Infrared Atmospheric Sounding Interferometer (IASI) sensor onboard the Metop satellites. These datasets were used to quantify NH&lt;sub&gt;3&lt;/sub&gt; temporal variabilities (trends, inter-annual, seasonal) over Toronto to assess the observational footprint of the FTIR measurements, and two case studies of pollution events due to transport of biomass burning plumes.&lt;/p&gt; &lt;p&gt;All three timeseries showed increasing trends in NH&lt;sub&gt;3&lt;/sub&gt; over Toronto: 3.34 &amp;#177; 0.44 %/year from 2002 to 2018 in the FTIR columns, 8.88 &amp;#177; 2.49 %/year from 2013 to 2017 in the surface in-situ data, and 8.78 &amp;#177; 0.84 %/year from 2008 to 2018 in the IASI columns. To assess the observational footprint of the FTIR NH&lt;sub&gt;3&lt;/sub&gt; columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was found for coincidence criterion of &amp;#8804; 50 km and &amp;#8804; 20 minutes, with r = 0.66 and a slope of 0.988 &amp;#177; 0.058. The FTIR column and in-situ measurements were standardized and correlated, with 24-day averages and monthly averages yielding correlation coefficients of r = 0.72 and r = 0.75, respectively.&lt;br&gt;FTIR and IASI were also compared against the GEOS-Chem model, run at 2&amp;#176; by 2.5&amp;#176; resolution, to assess model performance and investigate correlation of the model output with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (domain spanning from 35&amp;#176;N to 53&amp;#176;N, and 93.75&amp;#176;W to 63.75&amp;#176;W) resulted in r = 0.62, and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r&lt;sup&gt;2&lt;/sup&gt; = 0.38, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r&lt;sup&gt;2&lt;/sup&gt; = 0.26, indicating that a finer spatial resolution is needed to adequately model the variability of NH&lt;sub&gt;3&lt;/sub&gt;. This study also examines two case studies of NH&lt;sub&gt;3&lt;/sub&gt; enhancements due to biomass burning plumes, in August 2014 and May 2016. In these events, enhancements in both the total columns and surface NH3, were observed.&lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt;

scholarly journals DERIVING INHERENT OPTICAL PROPERTIES FROM MERIS IMAGERY AND IN SITU MEASUREMENT USING QUASI-ANALYTICAL ALGORITHM

2014 ◽  
Vol 10 (1) ◽  
Author(s):  
Wiwin Ambarwulan ◽  
Widiatmaka ◽  
Syarif Budhiman
Keyword(s):  
Optical Properties ◽  
Optical Measurement ◽  
In Situ Measurements ◽  
In Situ Measurement ◽  
Coefficient Of Determination ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Inherent Optical Properties ◽  
Analytical Algorithm

The  paper  describes inherent optical properties  (IOP)  of  the  Berau  coastal  waters  derived from in  situ measurements  and Medium  Resolution  Imaging  Spectrometer  (MERIS) satellite  data. Field  measurements  of optical  water,  total  suspended  matter  (TSM), and  chlorophyll-a  (Chl-a) concentrations were carried out during the dry season of 2007. During this periode, only four MERISdata were  coincided with in  situ measurements on 31 August  2007. The MERIS  top-of-atmosphere radiances were atmospherically corrected using the MODTRAN radiative transfer model. The in situ optical  measurement  have  been  processed  into apparent optical properties  (AOP) and sub  surface irradiance. The remote sensing reflectance of in situ measurement as well as MERIS data were inverted into  the  IOP  using quasi-analytical algorithm  (QAA).  The  result  indicated  that coefficient  of determination (R 2) of backscattering coefficients of suspended particles (bbp) increased with increasing wavelength,  however  the  R2 of  absorption  spectra  of  phytoplankton  (aph)  decreased  with  increasing wavelength.

scholarly journals DISCRIMINATION AND CHARACTERIZATION OF PROMINENT DESERTIC VEGETATIONS USING HYPERSPECTRAL IMAGING DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 363-368 ◽  
Author(s):  
S. L. Borana ◽  
S. K. Yadav ◽  
R. T. Paturkar
Keyword(s):  
Hyperspectral Imaging ◽  
Tree Species ◽  
Vegetation Type ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Imaging Data ◽  
Area Index ◽  
Vegetation Species

<p><strong>Abstract.</strong> Imaging Hyperspectral data are advent as potential solutions in modeling, discrimination and mapping of vegetation species. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, chlorophyll, and leaf nutrient concentration. Estimation of these vegetation parameters has been made possible by calculating various vegetation indices (VIs), usually by ratioing, differencing, ratioing differences and combinations of suitable spectral band. This paper presents a ground-based hyperspectral imaging system for characterizing vegetation spectral features. In this study, a ground-based hyperspectral imaging data (AISA VNIR 400&amp;ndash;960&amp;thinsp;nm, Spectral Resolution @ 2.5&amp;thinsp;nm) was used for spectral vegetation discrimination and characterization of natural desertic tree species. This study assessed the utility of hyperspectral imagery of 240 narrow bands in discrimination and classification of desert tree species in Jodhpur region using ENVI software. Vegetation indices derived from hyperspectral images used in the Analysis for tree species classification discrimination study. Prominent occurring two desertic tree species, viz., Neem and Babul in Jodhpur region could be effectively discriminated. Study demonstrated the potential utility of narrow spectral bands of Hyperspectral Imaging data in discriminating vegetation species in a desertic terrain.</p>

scholarly journals The Capability of Integrating Optical and Microwave Data for Detecting Soil Moisture in an Oasis Region

2020 ◽  
Vol 12 (9) ◽  
pp. 1358 ◽  
Author(s):  
Shuai Huang ◽  
Jianli Ding ◽  
Bohua Liu ◽  
Xiangyu Ge ◽  
Jinjie Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Empirical Relationship ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Ratio Model ◽  
Retrieval Model ◽  
Backscattering Coefficient ◽  
Strong Control

In the earth ecosystem, surface soil moisture is an important factor in the process of energy exchange between land and atmosphere, which has a strong control effect on land surface evapotranspiration, water migration, and carbon cycle. Soil moisture is particularly important in an oasis region because of its fragile ecological environment. Accordingly, a soil moisture retrieval model was conducted based on Dubois model and ratio model. Based on the Dubois model, the in situ soil roughness was used to simulate the backscattering coefficient of bare soil, and the empirical relationship was established with the measured soil moisture. The ratio model was used to eliminate the backscattering contribution of vegetation, in which three vegetation indices were used to characterize vegetation growth. The results were as follows: (1) the Dubois model was used to calibrate the unknown parameters of the ratio model and verified the feasibility of the ratio model to simulate the backscattering coefficient. (2) All three vegetation indices (Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Enhanced Vegetation Index (EVI)) can represent the scattering characteristics of vegetation in an oasis region, but the VWC vegetation index is more suitable than the others. (3) Based on the Dubois model and ratio model, the soil moisture retrieval model was conducted, and the in situ soil moisture was used to analyze the accuracy of the simulated soil moisture, which found that the soil moisture retrieval accuracy is the highest under VWC vegetation index, and the coefficient of determination is 0.76. The results show that the soil moisture retrieval model conducted on the Dubois model and ratio model is feasible.

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