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Research article - Peer-reviewed, 2022

Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation

Saarela, Svetlana; Hol, Soren; Healey, Sean P.; Patterson, Paul L.; Yang, Zhiqiang; Andersen, Hans-Erik; Dubayah, Ralph O.; Qi, Wenlu; Duncanson, Laura I.; Armston, John D.; Gobakken, Terje; Naesset, Erik


NASA's Global Ecosystem Dynamics Investigation (GEDI) mission offers data for temperate and pan-tropical estimates of aboveground forest biomass (AGB). The spaceborne, full-waveform LiDAR from GEDI provides sample footprints of canopy structure, expected to cover about 4% of the land area following two years of operation. Several options are available for estimating AGB at different geographical scales. Using GEDI sample data alone, gridded biomass predictions are based on hybrid inference which correctly propagates errors due to the modeling and accounts for sampling variability, but this method requires at least two GEDI tracks in the area of interest. However, there are significant gaps in GEDI coverage and in some areas of interest GEDI data may need to be combined with other wall-to-wall remotely sensed (RS) data, such as those from multispectral or SAR sensors. In these cases, we may employ hierarchical model-based (HMB) inference that correctly considers the additional model errors that result from relating GEDI data to the wallto-wall data. Where predictions are possible from both hybrid and HMB inference the question arises which framework to choose, and under what circumstances? In this paper, we make progress towards answering these questions by comparing the performance of the two prediction frameworks under conditions relevant for the GEDI mission. Conventional model-based (MB) inference with wall-to-wall TanDEM-X data was applied as a baseline prediction framework, which does not involve GEDI data at all. An important feature of the study was the comparison of AGB predictors in terms of both standard deviation (SD: the square root of variance) and root mean square error (RMSE: the square root of mean square error - MSE). Since, in model-based inference, the true AGB in an area of interest is a random variable, comparisons of the performance of prediction frameworks should preferably be made in terms of their RMSEs. However, in practice only the SD can be estimated based on empirical survey data, and thus it is important also to study whether or not the difference between the two uncertainty measures is small or large under conditions relevant for the GEDI mission. Our main findings were that: (i) hybrid and HMB prediction typically resulted in smaller RMSEs than conventional MB prediction although the difference between the three frameworks in terms of SD often was small; (ii) in most cases the difference between hybrid and HMB inference was small in terms of both RMSE and SD; (iii) the RMSEs for all frameworks was substantially larger than the SDs in small study areas whereas the two uncertainty measures were similar in large study areas, and; (iv) spatial autocorrelation of model residual errors had a large effect on the RMSEs of AGB predictors, especially in small study areas. We conclude that hybrid inference is suitable in most GEDI applications for AGB assessment, due to its simplicity compared to HMB inference. However, where GEDI data are sparse HMB inference should be preferred.


Carbon monitoring; GEDI; Hierarchical model-based inference; Hybrid inference; Mean square error; Model-based inference; Remote sensing; TanDEM-X

Published in

Remote Sensing of Environment
2022, volume: 278, article number: 113074

Authors' information

Swedish University of Agricultural Sciences, Department of Forest Resource Management
Norwegian University of Life Sciences (NMBU)
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Healey, Sean P.
United States Forest Service
Patterson, Paul L.
United States Department of Agriculture (USDA)
Yang, Zhiqiang
United States Forest Service
Andersen, Hans-Erik
United States Department of Agriculture (USDA)
Dubayah, Ralph O.
University of Maryland College Park
Qi, Wenlu
University System of Maryland
Duncanson, Laura I.
University System of Maryland
Armston, John D.
University System of Maryland
Gobakken, Terje
Norwegian University of Life Sciences
Naesset, Erik
Norwegian University of Life Sciences
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Umeå University
Swedish University of Agricultural Sciences, Department of Forest Resource Management

UKÄ Subject classification

Forest Science
Remote Sensing

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