| 1. |
- Egberth, Mikael, et al.
(författare)
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Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands
- 2017
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Ingår i: Carbon Balance and Management. - : BioMed Central (BMC). - 1750-0680. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Soil carbon and biomass depletion can be used to identify and quantify degraded soils,and by using remote sensing, there is potential to map soil conditions over large areas.Landsat 8 Operational Land Imager satellite data and airborne laser scanning datawere evaluated separately and in combination for modeling soil organic carbon, aboveground tree biomass and below ground tree biomass. The test site is situated in theLiwale district in southeastern Tanzania and is dominated by Miombo woodlands. Treedata from 15m radius field-surveyed plots and samples of soil carbon down to a depthof 30cm were used as reference data for tree biomass and soil carbon estimations.Cross-validated plot level error (RMSE) for predicting soil organic carbon was 28%using only Landsat 8, 26% using laser only, and 23% for the combination of the two.The plot level error for above ground tree biomass was 66% when using only Landsat8, 50% for laser and 49% for the combination of Landsat 8 and laser data. Results forbelow ground tree biomass were similar to above ground biomass. Additionally it wasfound that an early dry season satellite image was preferable for modelling biomasswhile images from later in the dry season were better for modelling soil carbon.The results show that laser data is superior to Landsat 8 when predicting both soilcarbon and biomass above and below ground in landscapes dominated by Miombowoodlands. Furthermore, the combination of laser data and Landsat data weremarginally better than using laser data only.
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| 2. |
- Saarela, Svetlana, et al.
(författare)
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Comparing frameworks for biomass prediction for the Global Ecosystem Dynamics Investigation
- 2022
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Ingår i: Remote Sensing of Environment. - : Elsevier. - 0034-4257 .- 1879-0704. ; 278
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Tidskriftsartikel (refereegranskat)abstract
- 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 wall-to-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.
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| 3. |
- Ståhl, Göran, et al.
(författare)
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Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time – and how this affects applications
- 2024
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Ingår i: Forest Ecosystems. - : Elsevier. - 2095-6355 .- 2197-5620. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics, and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decision-making. However, wall-to-wall information typically relies on model-based prediction, and several features of model-based prediction should be understood before extensively relying on this type of information. One such feature is that model-based predictors can be considered both unbiased and biased at the same time, which has important implications in several areas of application. In this discussion paper, we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed (or other) auxiliary data. From this point of view, model-based predictors are typically unbiased. Secondly, we show that for specific domains, identified based on their true values, the same model-based predictors can be considered biased, and sometimes severely so.We suggest distinguishing between conventional model-bias, defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted, and design-bias of model-based estimators, defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted. We show that model-based estimators (or predictors) are typically design-biased, and that there is a trend in the design-bias from overestimating small true values to underestimating large true values. Further, we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend. We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data.
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