scholarly journals Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation

2018 ◽  
Vol 10 (5) ◽  
pp. 667 ◽  
Author(s):  
Sarah Ehlers ◽  
Svetlana Saarela ◽  
Nils Lindgren ◽  
Eva Lindberg ◽  
Mattias Nyström ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Composite Estimation ◽  
Forest Attributes

scholarly journals From small-scale forest structure to Amazon-wide carbon estimates

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Edna Rödig ◽  
Nikolai Knapp ◽  
Rico Fischer ◽  
Friedrich J. Bohn ◽  
Ralph Dubayah ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Biomass ◽  
3D Structure ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Small Scale ◽  
Frequency Distributions ◽  
Forest Attributes ◽  
Detailed Interpretation ◽  
Remote Sensing Techniques

Abstract Tropical forests play an important role in the global carbon cycle. High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a challenge how to interpret such information for the assessment of forest biomass and productivity. Here, we develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. This detailed interpretation of remote sensing data improves estimates of forest attributes by 20–43% as compared to (conventional) estimates using mean canopy height. The inclusion of forest modeling has a high potential to close a missing link between remote sensing measurements and the 3D structure of forests, and may thereby improve continent-wide estimates of biomass and productivity.

scholarly journals Assessing Error Correlations in Remote Sensing-Based Estimates of Forest Attributes for Improved Composite Estimation

2017 ◽  
Author(s):  
Sarah Ehlers ◽  
Svetlana Saarela ◽  
Nils Lindgren ◽  
Eva Lindberg ◽  
Mattias Nyström ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Laser Scanning ◽  
Standard Procedure ◽  
Coniferous Forest ◽  
Test Site ◽  
Sources Of Information ◽  
Mean Diameter ◽  
Composite Estimation ◽  
Different Sources

Today, non-expensive remote sensing (RS) data from different sensors and platforms can be obtained at short intervals and be used for assessing several kinds of forest characteristics at the level of plots, stands and landscapes. Methods such as composite estimation and data assimilation can be used for combining the different sources of information to obtain up-to-date and precise estimates of the characteristics of interest. In composite estimation a standard procedure is to assign weights to the different individual estimates inversely proportional to their variance. However, in case the estimates are correlated, the correlations must be considered in assigning weights or otherwise a composite estimator may be inefficient and its variance be underestimated. In this study we assessed the correlation of plot level estimates of forest characteristics from different RS datasets, between assessments using the same type of sensor as well as across different sensors. The RS data evaluated were SPOT-5 multispectral data, 3D airborne laser scanning data, and TanDEM-X interferometric radar data. Studies were made for plot level mean diameter, mean height, and growing stock volume. All data were acquired from a test site dominated by coniferous forest in southern Sweden. We found that the correlation between plot level estimates based on the same type of RS data were positive and strong, whereas the correlations between estimates using different sources of RS data were not as strong, and weaker for mean height than for mean diameter and volume. The implications of such correlations in composite estimation are demonstrated and it is discussed how correlations may affect results from data assimilation procedures.

Model-assisted estimation of forest attributes exploiting remote sensing information to handle spatial under-coverage

2021 ◽  
Vol 41 ◽  
pp. 100472
Author(s):  
Sara Franceschi ◽  
Gherardo Chirici ◽  
Lorenzo Fattorini ◽  
Francesca Giannetti ◽  
Piermaria Corona
Keyword(s):  
Remote Sensing ◽  
Forest Attributes

Phased Arrays for Radio Astronomy, Remote Sensing, and Satellite Communications

2018 ◽  
Author(s):  
Karl F. Warnick ◽  
Rob Maaskant ◽  
Marianna V. Ivashina ◽  
David B. Davidson ◽  
Brian D. Jeffs
Keyword(s):  
Remote Sensing ◽  
Radio Astronomy ◽  
Phased Arrays ◽  
Satellite Communications

Hyperspectral Imaging Remote Sensing

2016 ◽  
Author(s):  
Dimitris Manolakis ◽  
Ronald Lockwood ◽  
Thomas Cooley
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging
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