scholarly journals Assessing the near surface sensitivity of SCIAMACHY atmospheric CO<sub>2</sub> retrieved using (FSI) WFM-DOAS

2007 ◽  
Vol 7 (13) ◽  
pp. 3597-3619 ◽  
Author(s):  
M. P. Barkley ◽  
P. S. Monks ◽  
A. J. Hewitt ◽  
T. Machida ◽  
A. Desai ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
The United States ◽  
Atmospheric Co2 ◽  
Carbon Surface ◽  
Near Surface ◽  
Surface Sensitivity ◽  
Vegetation Activity

Abstract. Satellite observations of atmospheric CO2 offer the potential to identify regional carbon surface sources and sinks and to investigate carbon cycle processes. The extent to which satellite measurements are useful however, depends on the near surface sensitivity of the chosen sensor. In this paper, the capability of the SCIAMACHY instrument on board ENVISAT, to observe lower tropospheric and surface CO2 variability is examined. To achieve this, atmospheric CO2 retrieved from SCIAMACHY near infrared (NIR) spectral measurements, using the Full Spectral Initiation (FSI) WFM-DOAS algorithm, is compared to in-situ aircraft observations over Siberia and additionally to tower and surface CO2 data over Mongolia, Europe and North America. Preliminary validation of daily averaged SCIAMACHY/FSI CO2 against ground based Fourier Transform Spectrometer (FTS) column measurements made at Park Falls, reveal a negative bias of about −2.0% for collocated measurements within ±1.0° of the site. However, at this spatial threshold SCIAMACHY can only capture the variability of the FTS observations at monthly timescales. To observe day to day variability of the FTS observations, the collocation limits must be increased. Furthermore, comparisons to in-situ CO2 observations demonstrate that SCIAMACHY is capable of observing a seasonal signal that is representative of lower tropospheric variability on (at least) monthly timescales. Out of seventeen time series comparisons, eleven have correlation coefficients of 0.7 or more, and have similar seasonal cycle amplitudes. Additional evidence of the near surface sensitivity of SCIAMACHY, is provided through the significant correlation of FSI derived CO2 with MODIS vegetation indices at over twenty selected locations in the United States. The SCIAMACHY/MODIS comparison reveals that at many of the sites, the amount of CO2 variability is coincident with the amount of vegetation activity. The presented analysis suggests that SCIAMACHY has the potential to detect CO2 variability within the lowermost troposphere arising from the activity of the terrestrial biosphere.

scholarly journals Assessing the near surface sensitivity of SCIAMACHY atmospheric CO<sub>2</sub> retrieved using (FSI) WFM-DOAS

2007 ◽  
Vol 7 (1) ◽  
pp. 2477-2530 ◽  
Author(s):  
M. P. Barkley ◽  
P. S. Monks ◽  
A. J. Hewitt ◽  
T. Machida ◽  
A. Desai ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
The United States ◽  
Atmospheric Co2 ◽  
Carbon Surface ◽  
Near Surface ◽  
Surface Sensitivity ◽  
Vegetation Activity

Abstract. Satellite observations of atmospheric CO2 offer the potential to identify regional carbon surface sources and sinks and to investigate carbon cycle processes. The extent to which satellite measurements are useful however, depends on the near surface sensitivity of the chosen sensor. In this paper, the capability of the SCIAMACHY instrument on board ENVISAT, to observe lower tropospheric and surface CO2 variability is examined. To achieve this, atmospheric CO2 retrieved from SCIAMACHY near infrared (NIR) spectral measurements, using the Full Spectral Initiation (FSI) WFM-DOAS algorithm, is compared to in situ aircraft observations over Siberia and additionally to tower and surface CO2 data over Mongolia, Europe and North America. Preliminary validation of daily averaged SCIAMACHY/FSI CO2 against ground based Fourier Transform Spectrometer (FTS) column measurements made at Park Falls, reveal a negative bias of about −2.0% for collocated measurements within ±1.0\\degree of the site. However, at this spatial threshold SCIAMACHY can only capture the variability of the FTS observations at monthly timescales. To observe day to day variability of the FTS observations, the collocation limits must be increased. Furthermore, comparisons to in-situ CO2 observations demonstrate that SCIAMACHY is capable of observing lower tropospheric variability on (at least) monthly timescales. Out of seventeen time series comparisons, eleven have correlation coefficients of 0.7 or more, and have similar seasonal cycle amplitudes. Additional evidence of the near surface sensitivity of SCIAMACHY, is provided through the significant correlation of FSI derived CO2 with MODIS vegetation indices at over twenty selected locations in the United States. The SCIAMACHY/MODIS comparison reveals that at many of the sites, the amount of CO2 variability is coincident with the amount of vegetation activity. It is evident, from this analysis, that SCIAMACHY therefore has the potential to detect CO2 variability within the lowermost troposphere arising from the activity of the terrestrial biosphere.

scholarly journals Analysis of temporal and spatial variability of atmospheric CO<sub>2</sub> concentration within Paris from the GreenLITE™ laser imaging experiment

2019 ◽  
Vol 19 (22) ◽  
pp. 13809-13825 ◽  
Author(s):  
Jinghui Lian ◽  
François-Marie Bréon ◽  
Grégoire Broquet ◽  
T. Scott Zaccheo ◽  
Jeremy Dobler ◽  
...  
Keyword(s):  
Urban Canopy ◽  
Temporal Variations ◽  
Horizontal Resolution ◽  
Atmospheric Co2 ◽  
Laser Imaging ◽  
Near Surface ◽  
Co2 Concentrations ◽  
Temporal And Spatial ◽  
The City

Abstract. In 2015, the Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE™) measurement system was deployed for a long-duration experiment in the center of Paris, France. The system measures near-surface atmospheric CO2 concentrations integrated along 30 horizontal chords ranging in length from 2.3 to 5.2 km and covering an area of 25 km2 over the complex urban environment. In this study, we use this observing system together with six conventional in situ point measurements and the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and two urban canopy schemes (Urban Canopy Model – UCM; Building Effect Parameterization – BEP) at a horizontal resolution of 1 km to analyze the temporal and spatial variations in CO2 concentrations within the city of Paris and its vicinity for the 1-year period spanning December 2015 to November 2016. Such an analysis aims at supporting the development of CO2 atmospheric inversion systems at the city scale. Results show that both urban canopy schemes in the WRF-Chem model are capable of reproducing the seasonal cycle and most of the synoptic variations in the atmospheric CO2 point measurements over the suburban areas as well as the general corresponding spatial differences in CO2 concentration that span the urban area. However, within the city, there are larger discrepancies between the observations and the model results with very distinct features during winter and summer. During winter, the GreenLITE™ measurements clearly demonstrate that one urban canopy scheme (BEP) provides a much better description of temporal variations and horizontal differences in CO2 concentrations than the other (UCM) does. During summer, much larger CO2 horizontal differences are indicated by the GreenLITE™ system than both the in situ measurements and the model results, with systematic east–west variations.

scholarly journals Analysis of temporal and spatial variability of atmospheric CO<sub>2</sub> concentration within Paris from the GreenLITE<sup>TM</sup> laser imaging experiment

2019 ◽  
Author(s):  
Jinghui Lian ◽  
François-Marie Bréon ◽  
Grégoire Broquet ◽  
T. Scott Zaccheo ◽  
Jeremy Dobler ◽  
...  
Keyword(s):  
Urban Canopy ◽  
Temporal Variations ◽  
Horizontal Resolution ◽  
Atmospheric Co2 ◽  
Laser Imaging ◽  
Near Surface ◽  
Co2 Concentrations ◽  
Temporal And Spatial ◽  
The City

Abstract. In 2015, the Greenhouse gas Laser Imaging Tomography Experiment (GreenLITETM) measurement system was deployed for a long-duration experiment in the center of Paris, France. The system measures near-surface atmospheric CO2 concentrations integrated along 30 horizontal chords ranging in length from 2.3 km to 5.2 km and covering an area of 25 km2 over the complex urban environment. In this study, we use this observing system together with six conventional in-situ point measurements and the WRF-Chem model coupled with two urban canopy schemes (UCM, BEP) at a horizontal resolution of 1 km to analyze the temporal and spatial variations of CO2 concentrations within the Paris city and its vicinity for the 1-year period spanning December 2015 to November 2016. Such an analysis aims at supporting the development of CO2 atmospheric inversion systems at the city scale. Results show that both urban canopy schemes in the WRF-Chem model are capable of reproducing the seasonal cycle and most of the synoptic variations in the atmospheric CO2 point measurements over the suburban areas, as well as the general corresponding spatial differences in CO2 concentration that span the urban area. However, within the city, there are larger discrepancies between the observations and the model results with very distinct features during winter and summer. During winter, the GreenLITETM measurements clearly demonstrate that one urban canopy scheme (BEP) provides a much better description of temporal variations and horizontal differences in CO2 concentrations than the other (UCM) does. During summer, much larger CO2 horizontal differences are indicated by the GreenLITETM system than both the in-situ measurements and the model results, with systematic east-west variations.

scholarly journals A multilayer physically based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow

2017 ◽  
Vol 11 (6) ◽  
pp. 2633-2653 ◽  
Author(s):  
Francois Tuzet ◽  
Marie Dumont ◽  
Matthieu Lafaysse ◽  
Ghislain Picard ◽  
Laurent Arnaud ◽  
...  
Keyword(s):  
Near Infrared ◽  
Solar Spectrum ◽  
Direct Impact ◽  
Near Surface ◽  
Deposition Fluxes ◽  
Radiative Impacts ◽  
Direct And Indirect Impacts ◽  
Indirect Impacts ◽  
Total Impact

Abstract. Light-absorbing impurities (LAIs) decrease snow albedo, increasing the amount of solar energy absorbed by the snowpack. Its most intuitive and direct impact is to accelerate snowmelt. Enhanced energy absorption in snow also modifies snow metamorphism, which can indirectly drive further variations of snow albedo in the near-infrared part of the solar spectrum because of the evolution of the near-surface snow microstructure. New capabilities have been implemented in the detailed snowpack model SURFEX/ISBA-Crocus (referred to as Crocus) to account for impurities' deposition and evolution within the snowpack and their direct and indirect impacts. Once deposited, the model computes impurities' mass evolution until snow melts out, accounting for scavenging by meltwater. Taking advantage of the recent inclusion of the spectral radiative transfer model TARTES (Two-stream Analytical Radiative TransfEr in Snow model) in Crocus, the model explicitly represents the radiative impacts of light-absorbing impurities in snow. The model was evaluated at the Col de Porte experimental site (French Alps) during the 2013–2014 snow season against in situ standard snow measurements and spectral albedo measurements. In situ meteorological measurements were used to drive the snowpack model, except for aerosol deposition fluxes. Black carbon (BC) and dust deposition fluxes used to drive the model were extracted from simulations of the atmospheric model ALADIN-Climate. The model simulates snowpack evolution reasonably, providing similar performances to our reference Crocus version in terms of snow depth, snow water equivalent (SWE), near-surface specific surface area (SSA) and shortwave albedo. Since the reference empirical albedo scheme was calibrated at the Col de Porte, improvements were not expected to be significant in this study. We show that the deposition fluxes from the ALADIN-Climate model provide a reasonable estimate of the amount of light-absorbing impurities deposited on the snowpack except for extreme deposition events which are greatly underestimated. For this particular season, the simulated melt-out date advances by 6 to 9 days due to the presence of light-absorbing impurities. The model makes it possible to apportion the relative importance of direct and indirect impacts of light-absorbing impurities on energy absorption in snow. For the snow season considered, the direct impact in the visible part of the solar spectrum accounts for 85 % of the total impact, while the indirect impact related to accelerated snow metamorphism decreasing near-surface specific surface area and thus decreasing near-infrared albedo accounts for 15 % of the total impact. Our model results demonstrate that these relative proportions vary with time during the season, with potentially significant impacts for snowmelt and avalanche prediction.

scholarly journals A multi-layer physically-based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow

2017 ◽  
Author(s):  
Francois Tuzet ◽  
Marie Dumont ◽  
Matthieu Lafaysse ◽  
Ghislain Picard ◽  
Laurent Arnaud ◽  
...  
Keyword(s):  
Near Infrared ◽  
Solar Spectrum ◽  
Snow Melt ◽  
Near Surface ◽  
Deposition Fluxes ◽  
Radiative Impacts ◽  
Direct And Indirect Impacts ◽  
Indirect Impacts ◽  
Total Impact

Abstract. Light-absorbing impurities decrease snow albedo, increasing the amount of solar energy absorbed by the snowpack. Its most intuitive and direct impact is to accelerate snow melt. Enhanced energy absorption in snow also modifies snow metamorphism, which can indirectly drive further variations of snow albedo in the near-infrared part of the solar spectrum because of the evolution of the near-surface snow microstructure. New capabilities have been implemented in the detailed snowpack model SURFEX/ISBA-Crocus (hereafter referred as Crocus) to account for impurities deposition and evolution within the snowpack and their direct and indirect impacts. Once deposited, the model computes impurities mass evolution until snow melts out, accounting for scavenging by melt water. Taking benefits of the recent inclusion of the spectral radiative transfer model TARTES in Crocus, the model explicitly represents the radiative impacts of light-absorbing impurities in snow. The model was evaluated at Col de Porte experimental site (French Alps) during the 2013–2014 snow season, against in-situ standard snow measurements and spectral albedo measurements. In-situ meteorological measurements were used to drive the snowpack model, except for aerosol deposition fluxes. Black carbon and dust deposition fluxes used to drive the model were extracted from simulations of the atmospheric model ALADIN-Climate. The model simulates reasonably snowpack evolution in term of snow depth, snow water equivalent and near-surface specific surface area. Indeed, the model performances are not deteriorated compared to our reference Crocus version, while explicitly representing the impact of light-absorbing impurities. We show that the deposition fluxes from ALADIN-Climate model provide a reasonable estimate of the amount of light-absorbing impurities deposited on the snowpack except for extreme deposition events which are greatly underestimated. For this particular season, the simulated melt-out date advances from 6 to 9 days due to the presence of light-absorbing impurities. The model makes it possible to apportion the relative importance of direct and indirect impacts of light-absorbing impurities on energy absorption in snow. For the snow season considered, the direct impact in the visible part of the solar spectrum accounts for 85 % of the total impact, while the indirect impact related to accelerated snow metamorphism decreasing near-surface specific surface area and thus decreasing near-infrared albedo, accounts for 15 % of the total impact. Our model results demonstrate that these relative proportions vary with time during the season, with potentially significant impacts for snow melt and avalanche prediction.

scholarly journals Estimating root zone soil moisture using near-surface observations from SMOS

2014 ◽  
Vol 18 (1) ◽  
pp. 139-154 ◽  
Author(s):  
T. W. Ford ◽  
E. Harris ◽  
S. M. Quiring
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
Surface Soil ◽  
Root Zone ◽  
The United States ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Cross Correlation Analysis

Abstract. Satellite-derived soil moisture provides more spatially and temporally extensive data than in situ observations. However, satellites can only measure water in the top few centimeters of the soil. Root zone soil moisture is more important, particularly in vegetated regions. Therefore estimates of root zone soil moisture must be inferred from near-surface soil moisture retrievals. The accuracy of this inference is contingent on the relationship between soil moisture in the near-surface and the soil moisture at greater depths. This study uses cross correlation analysis to quantify the association between near-surface and root zone soil moisture using in situ data from the United States Great Plains. Our analysis demonstrates that there is generally a strong relationship between near-surface (5–10 cm) and root zone (25–60 cm) soil moisture. An exponential decay filter is used to estimate root zone soil moisture using near-surface soil moisture derived from the Soil Moisture and Ocean Salinity (SMOS) satellite. Root zone soil moisture derived from SMOS surface retrievals is compared to in situ soil moisture observations in the United States Great Plains. The SMOS-based root zone soil moisture had a mean R2 of 0.57 and a mean Nash–Sutcliffe score of 0.61 based on 33 stations in Oklahoma. In Nebraska, the SMOS-based root zone soil moisture had a mean R2 of 0.24 and a mean Nash–Sutcliffe score of 0.22 based on 22 stations. Although the performance of the exponential filter method varies over space and time, we conclude that it is a useful approach for estimating root zone soil moisture from SMOS surface retrievals.

scholarly journals Evaluation of UV-visible MAX-DOAS aerosol profiling products by comparison with ceilometer, sun photometer, and in situ observations in Vienna, Austria

2021 ◽  
Author(s):  
Stefan F. Schreier ◽  
Tim Bösch ◽  
Andreas Richter ◽  
Kezia Lange ◽  
Michael Revesz ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Correlation Coefficients ◽  
Near Surface ◽  
Fall Season ◽  
Different Seasons ◽  
Uv Visible ◽  
Sun Photometer ◽  
Relative Differences ◽  
First Time

Abstract. Since May 2017 and August 2018, two ground-based MAX-DOAS (Multi AXis Differential Optical Absorption Spectroscopy) instruments have been continuously recording daytime spectral UV-visible measurements in the north-west (University of Natural Resources and Life Sciences (BOKU) site) and south (Arsenal site) of the Vienna city centre (Austria), respectively. In this study, aerosol extinction (AE) profiles, aerosol optical depth (AOD), and near-surface AE are retrieved from MAX-DOAS measurements recorded on cloud-free days applying the Bremen Optimal estimation REtrieval for Aerosols and trace gaseS (BOREAS) algorithm. For the first time, measurements of atmospheric profiles of pressure and temperature obtained from routinely performed sonde ascents are used to calculate box-air-mass-factors and weighting functions for different seasons. The performance of BOREAS was evaluated against co-located ceilometer, sun photometer, and in situ instrument observations covering all four seasons. The results showed that the vertical AE profiles retrieved from the BOKU UV-visible MAX-DOAS observations are in very good agreement with data from the co-located ceilometer, reaching correlation coefficients (R) of 0.91–0.99 (UV) and 0.85–0.98 (visible) during fall, winter, and spring seasons. Moreover, AE extracted using the lowest part of MAX-DOAS vertical profiles (up to 100 m above ground) are highly consistent with near-surface ceilometer AE (R > 0.90 and linear regression slopes of ~0.90) during the fall season. A strong correlation is also found for the BOREAS-based AODs when compared to the AERONET ones. Notably, the highest correlation coefficients (R = 0.95 and R = 0.94 for UV and visible, respectively) were identified for the fall season. While high correlation coefficients are also found for the winter and spring seasons, the results are less reliable for measurements taken during summer. For the first time, the spatial variability of AOD and near-surface AE over the urban environment of Vienna is assessed by analyzing the retrieved and evaluated BOREAS aerosol profiling products in terms of different azimuth angles of the two MAX-DOAS instruments and for different seasons. We found that the relative differences of averaged AOD between different azimuth angles are 7–13 %, depending on the season. Larger relative differences of up to 32 % obtained for the different azimuthal directions are found for near-surface AE. This study revealed the strong capability of BOREAS to retrieve AE profiles, AOD, and near-surface AE over urban environments and demonstrated its use for identifying the spatial variability of aerosols, in addition to the temporal variation.

scholarly journals Evaluation of UV–visible MAX-DOAS aerosol profiling products by comparison with ceilometer, sun photometer, and in situ observations in Vienna, Austria

2021 ◽  
Vol 14 (8) ◽  
pp. 5299-5318
Author(s):  
Stefan F. Schreier ◽  
Tim Bösch ◽  
Andreas Richter ◽  
Kezia Lange ◽  
Michael Revesz ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Correlation Coefficients ◽  
Optimal Estimation ◽  
Optical Absorption Spectroscopy ◽  
Near Surface ◽  
Different Seasons ◽  
Uv Visible ◽  
Sun Photometer ◽  
Relative Differences

Abstract. Since May 2017 and August 2018, two ground-based MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments have been continuously recording daytime spectral UV–visible measurements in the northwest (University of Natural Resources and Life Sciences (BOKU) site) and south (Arsenal site), respectively, of the Vienna city center (Austria). In this study, vertical aerosol extinction (AE) profiles, aerosol optical depth (AOD), and near-surface AE are retrieved from MAX-DOAS measurements recorded on cloud-free days applying the Bremen Optimal estimation REtrieval for Aerosols and trace gaseS (BOREAS) algorithm. Measurements of atmospheric profiles of pressure and temperature obtained from routinely performed sonde ascents are used to calculate box-air-mass factors and weighting functions for different seasons. The performance of BOREAS was evaluated against co-located ceilometer, sun photometer, and in situ instrument observations covering all four seasons. The results show that the vertical AE profiles retrieved from the BOKU UV–visible MAX-DOAS observations are in very good agreement with data from the co-located ceilometer, reaching correlation coefficients (R) of 0.936–0.996 (UV) and 0.918–0.999 (visible) during the fall, winter, and spring seasons. Moreover, AE extracted using the lowest part of MAX-DOAS vertical profiles (up to 100 m above ground) is highly consistent with near-surface ceilometer AE (R>0.865 and linear regression slopes of 0.815–1.21) during the fall, winter, and spring seasons. A strong correlation is also found for the BOREAS-based AODs when compared to the AERONET ones. Notably, the highest correlation coefficients (R=0.953 and R=0.939 for UV and visible, respectively) were identified for the fall season. While high correlation coefficients are generally found for the fall, winter, and spring seasons, the results are less reliable for measurements taken during summer. For the first time, the spatial variability of AOD and near-surface AE over the urban environment of Vienna is assessed by analyzing the retrieved and evaluated BOREAS aerosol profiling products in terms of different azimuth angles of the two MAX-DOAS instruments and for different seasons. We found that the relative differences of averaged AOD between different azimuth angles are 7–13 %, depending on the season. Larger relative differences of up to 32 % are found for near-surface AE in the different azimuthal directions. This study revealed the strong capability of BOREAS to retrieve AE profiles, AOD, and near-surface AE over urban environments and demonstrated its use for identifying the spatial variability of aerosols in addition to the temporal variation.

scholarly journals Spectral Feature Selection Optimization for Water Quality Estimation

2019 ◽  
Vol 17 (1) ◽  
pp. 272 ◽  
Author(s):  
Manh Van Nguyen ◽  
Chao-Hung Lin ◽  
Hone-Jay Chu ◽  
Lalu Muhamad Jaelani ◽  
Muhammad Aldila Syariz
Keyword(s):  
Near Infrared ◽  
Spectral Feature ◽  
Correlation Coefficients ◽  
Estimation Model ◽  
Chl A ◽  
Spectral Bands ◽  
Spectral Models ◽  
Multispectral Satellite Images ◽  
Estimation Models

The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 mg · m − 3 to 6.37 mg · m − 3 , and the Pearson’s correlation coefficients between the predicted and in situ Chl- a improve from 0.56 to 0.89.

Combining SPOT 5 imagery with plotwise and standwise forest data to estimate volume and biomass in mountainous coniferous site

2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Petar Dimitrov ◽  
Eugenia Roumenina
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
Test Site ◽  
National Forest Inventory ◽  
Linear Regression Models ◽  
Infrared Band ◽  
Topographic Correction ◽  
Maximal Correlation ◽  
Spot 5

AbstractIn this study, regression-based prediction of volume and aboveground biomass (AGB) of coniferous forests in a mountain test site was conducted. Two datasets — one with applied topographic correction and one without applied topographic correction — consisting of four spectral bands and six vegetation indices were generated from SPOT 5 multispectral image. The relationships between these data and ground data from field plots and national forest inventory polygons were examined. Strongest correlations of volume and AGB were observed with the near infrared band, regardless of the topographic correction. The maximal correlation coefficients when using plotwise data were −0.83 and −0.84 for the volume and AGB, respectively. The maximal correlation with standwise data was −0.63 for both parameters. The SCS+C topographic correction did not significantly affect the correlations between spectral data and forest parameters, but visually removed much of the topographically induced shading. Simple linear regression models resulted in relative RMSE of 32–33% using the plotwise data, and 43–45% using the standwise data. The importance of the source and the methodology used to obtain ground data for the successful modelling was pointed out.

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