Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images

Wang, Guangxing; Oyana, Tonny J.; Zhang, Maozhen; Adu-Prah, Samuel; Si-qi, Zeng; Lin, Hui; Se, Jiyun

doi:10.1016/j.foreco.2009.06.056

Cited by 67 publications

(109 citation statements)

References 40 publications

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“…Usually, remotely sensed data are available at multiple spatial resolutions, while field observations are collected using limited and fixed sizes of sample plots. The inconsistency of spatial resolutions between sample plot and remotely sensed data will result in a great challenge for mapping and accuracy assessment of forest carbon density [11,20,27,[31][32][33][34][35]. There have only been few relevant studies reported in this field.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, an image-based SGCS algorithm developed by Wang et al [20,27,47,48] was utilized to generate forest carbon density maps at the spatial resolutions of Landsat 30 m × 30 m and MODIS 250 m × 250 m, 500 m × 500 m and 1000 m × 1000 m. In this algorithm, it is assumed that a study area consists of N pixels that have the same spatial resolution with the reference data from the sample plots (Figure 3a). It is also assumed that forest carbon density is a random process that consists of random variables z(u) that are spatially auto-correlated with each other.…”

Section: Spatial Co-simulation Algorithms To Generate Forest Carbon Dmentioning

confidence: 99%

“…The distance that corresponds to the maximum variance is called the range parameter of the model implying the maximum distance of spatial auto-correlation. To obtain a realization at each pixel, first a spatial auto-correlation model of forest carbon density must be developed by calculating an experimental variogram-spatial variability function over the distance between data locations given a direction and then fitting the variogram using theoretical variance functions such as spherical, Gaussian and exponential model [20,27,47,48]. The obtained variogram accounts for the spatial variability of forest carbon density.…”

Section: Spatial Co-simulation Algorithms To Generate Forest Carbon Dmentioning

confidence: 99%

“…In addition, an image-based up-scaling algorithm SGBCS (Figure 3b) was utilized to generate the maps of forest carbon density at the spatial resolutions of 250 mˆ250 m, 500 mˆ500 m and 1000 mˆ1000 m by combining and scaling up the forest carbon density plot data and the Landsat image at the original spatial resolution of 30 mˆ30 m [20,27,48]. The block co-simulation is similar to the above SGCS.…”

Section: Spatial Co-simulation Algorithms To Generate Forest Carbon Dmentioning

confidence: 99%

“…Regression is a most commonly used method that combines plot data and images to map forest carbon density [9, [17][18][19], but this method often leads to illogical negative and extremely large estimates [19]. Wang et al [20] proposed an image-based co-simulation algorithm to map forest carbon density by combining sample plot and image data. This method overcomes some of the gaps that currently exist in the estimation and product quality assessment of forest carbon density.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design

et al. 2016

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Abstract:Combining sample plot and image data has been widely used to map forest carbon density at local, regional, national and global scales. When mapping is conducted using multiple spatial resolution images at different scales, field observations have to be collected at the corresponding resolutions to match image values in pixel sizes. Given a study area, however, to save time and cost, field observations are often collected from sample plots having a fixed size. This will lead to inconsistency of spatial resolutions between sample plots and image pixels and impede the mapping and product quality assessment. In this study, a methodological framework was proposed to conduct mapping and accuracy assessment of forest carbon density at four spatial resolutions by combining remotely sensed data and reference values of sample plots from a systematical, nested and clustering sampling design. This design led to one field observation dataset at a 30 m spatial resolution sample plot level and three other reference datasets by averaging the observations from three, five and seven sample plots within each of 250 m and 500 m sub-blocks and 1000 m blocks, respectively. The datasets matched the pixel values of a Landsat 8 image and three MODIS products. A sequential Gaussian co-simulation (SGCS) and a sequential Gaussian block co-simulation (SGBCS), an upscaling algorithm, were employed to map forest carbon density at the spatial resolutions. This methodology was tested for mapping forest carbon density in Huang-Feng-Qiao forest farm of You County in Eastern Hunan of China. The results showed that: First, all of the means of predicted forest carbon density values at four spatial resolutions fell in the confidence intervals of the reference data at a significance level of 0.05. Second, the systematical, nested and clustering sampling design provided the potential to obtain spatial information of forest carbon density at multiple spatial resolutions. Third, the relative root mean square error (RMSE) of predicted values at the plot level was much greater than those at the sub-block and block levels. Moreover, the accuracies of the up-scaled estimates were much higher than those from previous studies. In addition, at the same spatial resolution, SGCSWA (scaling up the SGCS and Landsat derived 30 m resolution map using a window average (WA)) resulted in smallest relative RMSEs of up-scaled predictions, followed by combinations of Landsat images and SGBCS. The accuracies from both methods were significantly greater than those from the combinations of MODIS images and SGCS. Overall, this study implied that the combinations of Landsat 8 images and SGCSWA or SGBCS with the systematical, nested and clustering sampling design provided the potential to formulate a methodological framework to map forest carbon density and conduct accuracy assessment at multiple spatial resolutions. However, this methodology needs to be further refined and examined in other forest landscapes.

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Section: Discussionmentioning

confidence: 99%

Section: Spatial Co-simulation Algorithms To Generate Forest Carbon Dmentioning

confidence: 99%

Section: Spatial Co-simulation Algorithms To Generate Forest Carbon Dmentioning

confidence: 99%

Section: Spatial Co-simulation Algorithms To Generate Forest Carbon Dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design

et al. 2016

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Upscaling With Conditional Cosimulation for Mapping Above‐Ground Forest Carbon

Wang

Zhang

2014

Scale Issues in Remote Sensing

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Using near‐term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density

Lofton

Brentrup

Beck

et al. 2022

Ecological Applications

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Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian

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Mapping and spatial uncertainty analysis of forest vegetation carbon by combining national forest inventory data and satellite images

Cited by 67 publications

References 40 publications

Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design

Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design

Upscaling With Conditional Cosimulation for Mapping Above‐Ground Forest Carbon

Using near‐term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density

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