Uncertainties in mapping forest carbon in urban ecosystems

Abstract

Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density - 5.8, 4.6, 2.3, and 1.2 pt s/m, aerial optical NAIP (National Agricultural Imagery Program) imagery - 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7-11.0% of variation) than changing NAIP image resolution (causing 6.2-8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape.

Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

“…For example, Simard et al [71] found that the mean tree height was highly related to the aboveground biomass in Everglades National Park. Scale is a crucial issue of the remote-sensing data; the resolution of optical data and the density of LiDAR point cloud have a strong effect on the forest biomass [74,75]. We will explore the effect of remote sensing data with different scales on biomass estimation accuracy in the future.…”

The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data

“…Moreover, as a carbon sink, forest dynamics are closely related to the global carbon cycle and climate changes [2][3][4]. Canopy height is an important forest structure parameter for understanding the forest ecosystem, and has been used to estimate forest aboveground biomass and therefore model the global carbon stock and carbon dynamics [5][6][7][8].…”

Valuing urban green spaces in mitigating climate change: A city‐wide estimate of aboveground carbon stored in urban green spaces of China's Capital

“…The spatial variation in aboveground carbon density that we estimated across the central six districts of Beijing emphasized the importance of quantifying carbon stocks using fine‐resolution data. Fragmented urban development often leads to small green patches and isolated trees, and therefore, fine‐spatial resolution remote sensing data used in this study is particularly beneficial to illustrate the spatial variation in carbon storage (Chen et al, ; Mitchell et al, ). Due to the mixed‐pixel problem, each pixel is represented by the predominant land cover, and thus, maps from medium‐resolution remote sensing data such as Landsat could not capture the finely grained green spaces in urbanized areas (Davies et al, ; Raciti et al, ).…”