scholarly journals Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests

2010 ◽  
Vol 7 (8) ◽  
pp. 2531-2543 ◽  
Author(s):  
P. Köhler ◽  
A. Huth
Keyword(s):  
Remote Sensing ◽  
Rain Forest ◽  
Tropical Rain Forest ◽  
Forest Growth ◽  
Canopy Height ◽  
Radar Interferometry ◽  
Area Index ◽  
Forest Growth Model ◽  
Ground Truthing ◽  
Remote Sensing Techniques

Abstract. The canopy height h of forests is a key variable which can be obtained using air- or spaceborne remote sensing techniques such as radar interferometry or LIDAR. If new allometric relationships between canopy height and the biomass stored in the vegetation can be established this would offer the possibility for a global monitoring of the above-ground carbon content on land. In the absence of adequate field data we use simulation results of a tropical rain forest growth model to propose what degree of information might be generated from canopy height and thus to enable ground-truthing of potential future satellite observations. We here analyse the correlation between canopy height in a tropical rain forest with other structural characteristics, such as above-ground life biomass (AGB) (and thus carbon content of vegetation) and leaf area index (LAI) and identify how correlation and uncertainty vary for two different spatial scales. The process-based forest growth model FORMIND2.0 was applied to simulate (a) undisturbed forest growth and (b) a wide range of possible disturbance regimes typically for local tree logging conditions for a tropical rain forest site on Borneo (Sabah, Malaysia) in South-East Asia. In both undisturbed and disturbed forests AGB can be expressed as a power-law function of canopy height h (AGB = a · hb) with an r2 ~ 60% if data are analysed in a spatial resolution of 20 m × 20 m (0.04 ha, also called plot size). The correlation coefficient of the regression is becoming significant better in the disturbed forest sites (r2 = 91%) if data are analysed hectare wide. There seems to exist no functional dependency between LAI and canopy height, but there is also a linear correlation (r2 ~ 60%) between AGB and the area fraction of gaps in which the canopy is highly disturbed. A reasonable agreement of our results with observations is obtained from a comparison of the simulations with permanent sampling plot (PSP) data from the same region and with the large-scale forest inventory in Lambir. We conclude that the spaceborne remote sensing techniques such as LIDAR and radar interferometry have the potential to quantify the carbon contained in the vegetation, although this calculation contains due to the heterogeneity of the forest landscape structural uncertainties which restrict future applications to spatial averages of about one hectare in size. The uncertainties in AGB for a given canopy height are here 20–40% (95% confidence level) corresponding to a standard deviation of less than ± 10%. This uncertainty on the 1 ha-scale is much smaller than in the analysis of 0.04 ha-scale data. At this small scale (0.04 ha) AGB can only be calculated out of canopy height with an uncertainty which is at least of the magnitude of the signal itself due to the natural spatial heterogeneity of these forests.

scholarly journals Towards ground-truthing of spaceborne estimates of above-ground biomass and leaf area index in tropical rain forests

2010 ◽  
Vol 7 (3) ◽  
pp. 3227-3255 ◽  
Author(s):  
P. Köhler ◽  
A. Huth
Keyword(s):  
Rain Forest ◽  
Tropical Rain Forest ◽  
Forest Growth ◽  
Canopy Height ◽  
Above Ground Biomass ◽  
Area Index ◽  
Forest Growth Model ◽  
Undisturbed Forest ◽  
Ground Truthing ◽  
Remote Sensing Techniques

Abstract. The canopy height of forests is a key variable which can be obtained using air- or spaceborne remote sensing techniques such as radar interferometry or lidar. If new allometric relationships between canopy height and the biomass stored in the vegetation can be established this would offer the possibility for a global monitoring of the above-ground carbon content on land. In the absence of adequate field data we use simulation results of a tropical rain forest growth model to propose what degree of information might be generated from canopy height and thus to enable ground-truthing of potential future satellite observations. We here analyse the correlation between canopy height in a tropical rain forest with other structural characteristics, such as above-ground biomass (AGB) (and thus carbon content of vegetation) and leaf area index (LAI). The process-based forest growth model FORMIND2.0 was applied to simulate (a) undisturbed forest growth and (b) a wide range of possible disturbance regimes typically for local tree logging conditions for a tropical rain forest site on Borneo (Sabah, Malaysia) in South-East Asia. It is found that for undisturbed forest and a variety of disturbed forests situations AGB can be expressed as a power-law function of canopy height h (AGB=a·hb) with an r2~60% for a spatial resolution of 20 m×20 m (0.04 ha, also called plot size). The regression is becoming significant better for the hectare wide analysis of the disturbed forest sites (r2=91%). There seems to exist no functional dependency between LAI and canopy height, but there is also a linear correlation (r2~60%) between AGB and the area fraction in which the canopy is highly disturbed. A reasonable agreement of our results with observations is obtained from a comparison of the simulations with permanent sampling plot data from the same region and with the large-scale forest inventory in Lambir. We conclude that the spaceborne remote sensing techniques have the potential to quantify the carbon contained in the vegetation, although this calculation contains due to the heterogeneity of the forest landscape structural uncertainties which restrict future applications to spatial averages of about one hectare in size. The uncertainties in AGB for a given canopy height are here 20–40% (95% confidence level) corresponding to a standard deviation of less than ±10%. This uncertainty on the 1 ha-scale is much smaller than in the analysis of 0.04 ha-scale data. At this small scale (0.04 ha) AGB can only be calculated out of canopy height with an uncertainty which is at least of the magnitude of the signal itself due to the natural spatial heterogeneity of these forests.

Solar energy conversion efficiencies during succession of a tropical rain forest in Amazonia

1991 ◽  
Vol 7 (2) ◽  
pp. 233-242 ◽  
Author(s):  
Juan Guillermo Saldarriaga ◽  
Robert John Luxmoore
Keyword(s):  
Conversion Efficiency ◽  
Rain Forest ◽  
Tropical Rain Forest ◽  
Root Production ◽  
Successional Stage ◽  
Area Index ◽  
Clear Cut ◽  
Matter Production ◽  
The Mean ◽  
Conversion Efficiencies

ABSTRACTMean annual quantities of solar radiation absorbed during various stages of regeneration of a tropical rain forest in the upper Rio Negro valley of Colombia and Venezuela were estimated for the consecutive intervals between clear-cut and 1,3, 10, 20, 35, 60, 80 and 200 years of growth. Forest phytomass and litter fall data from each of these stages were used to calculate the mean annual net dry matter production per unit of absorbed photosynthelically active radiation (PAR), the PAR conversion efficiency. The quantities of PAR absorbed by the forest stands were calculated from the leaf area index values with an extinction coefficient for PAR of 0.74, a PAR albedo of 0.04, and an annual mean incoming PAR of 2.86GJ m-2y-1. Efficiency decreased with increase in successional stage. During the first 10 years of regrowth, the efficiency of conversion of PAR into above-ground phylomass averaged 0.23 g MJ-1, decreasing to 0.07 g MJ-1 over the following 50 years. Inclusion of annual root production in the calculations resulted in a small increase in PAR conversion efficiency; however, efficiency was more than doubled for some periods when the annual leaf and twig lillerfall were included. Efficiency values for above-ground production were much lower than PAR conversion efficiency values estimated for above-ground production of temperate forests.

scholarly journals Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest

2020 ◽  
Vol 12 (9) ◽  
pp. 1519 ◽  
Author(s):  
Sujit Madhab Ghosh ◽  
Mukunda Dev Behera ◽  
Somnath Paramanik
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Forest Canopy ◽  
Canopy Height ◽  
Series Data ◽  
Learning Models ◽  
Biophysical Parameters ◽  
Height Estimation ◽  
Area Index ◽  
Machine Learning Models

Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R2 value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R2 values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation.

scholarly journals Influencia del ángulo de observación en la estimación del índice de área foliar (LAI) mediante imágenes PROBA/CHRIS

2016 ◽  
pp. 45 ◽  
Author(s):  
J. Delegido ◽  
C. M. Meza ◽  
N. Pasqualotto ◽  
J. Moreno
Keyword(s):  
Remote Sensing ◽  
Viewing Angle ◽  
Area Index ◽  
Red Edge ◽  
The Subject ◽  
Remote Sensing Techniques ◽  
Normalized Difference Index ◽  
The Relationship ◽  
Chlorophyll Absorption ◽  
Sentinel 2

<p>The estimation of biophysical variables, such as the Leaf Area Index (LAI), using remote sensing techniques, is still the subject of numerous studies, since these variables allow obtaining valuable information on the vegetation status. In this work, we estimate LAI from multiangular PROBA/CHRIS images, by analyzing the reflectance measured in its 5 observation angles, for the bands centered in 665 and 705 nm. These wavelengths correspond to the chlorophyll absorption band and the Red-Edge region, respectively. The Normalized Difference Index (NDI) calculated from this wavelengths, shows good correlation with LAI and allows its remote sensing estimation and its applicability to the recently launched ESA Sentinel 2, thanks to its new bands in the Red-Edge. This research analyzed the influence on the acquisition geometry in the NDI, calibrating the relationship between this index and the LAI for each of the five observation angles in the PROBA / CHRIS images. As a result, we have obtained a relationship capable of providing LAI from the viewing angle and the NDI index.</p>

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