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

Abstract

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.

Modeling Potential C, N, H Content in Aboveground Biomass with Spectral Data from Sentinel 2a

“…The different satellite images provide very accurate information at the local, regional and national level if their methodology is followed correctly [28]. The biomass estimated through the NDVI and simple regression was 0.95 t ha -1 with an R 2 = 0.85, values slightly lower than those obtained by Lumbierres [46] using MODIS images from NDVI (1 to 10 t ha -1 ).…”

“…To make the most accurate C estimation, the spatial resolution of the satellite image becomes a key factor in validating models. A study done by Yan [28] working with resolutions between 30 and 1000 m 2 , used a model that showed high coefficients of variation as the image resolution decreased. The results of our investigation was 5.55 (CV) with a 20×20 m resolution, the difference due to the type of satellite used (Sentinel 2a).…”

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

“…For enhancing the correlations between forest carbon density and spectral variables, we retrieved 200 spectral variables from the Landsat 8 image and its transformation images, including 6 original bands, 6 band inversions, 30 two-band ratios, 60 three-band ratios, 30 difference vegetation indices, 14 simple vegetation indices, 6 principal component variables, and 48 texture variables from the gray level co-occurrence matrix of the image bands [3].…”

“…Then, the area-weighted RDMR was computed for each spectral band (Equation (8)). To assess the estimation accuracy of forest carbon density, the spectral variables for the images after TC were used as the independent variables and the forest carbon density of the measured sample plots was utilized as the dependent variable [3,12,13]. Support vector regression (SVR) was then adopted for modeling, and R 2 and RMSE were employed to evaluate the effects of the moving window sizes on the estimation of forest carbon density.…”

“…Forests play a crucial role in the biosphere, as they mitigate carbon concentration in the atmosphere and control global warming [1][2][3]. Accurately mapping forest carbon density at different scales is of great significance for reducing greenhouse effects [4] and maintaining environmental sustainability [4,5].…”