Aihua Li | Search USGS

USGS2017

Aerodynamic roughness length estimation with lidar and imaging spectroscopy in a shrub-dominated dryland

The aerodynamic roughness length (Z 0 m ) serves an important role in the flux exchange between the land surface and atmosphere. In this study, airborne lidar ( ALS ), terrestrial lidar ( TLS ), and imaging spectroscopy data were integrated to develop and test two approaches to estimate Z 0 m over a shrub dominated dryland study area in south-central Idaho, USA. Sensitivity of the two parameterization methods to estimate Z 0 m was analyzed. The comparison of eddy covariance-derived Z 0 m and remote sensing-derived Z 0 m showed that the accuracy of the estimated Z 0 m heavily depends on the estimation model and the representation of shrub (e.g., Artemisia tridentata subsp. wyomingensis) height in the models. The geometrical method (RA1994) led to 9 percent (~0.5 cm) and 25% (~1.1 cm) errors at site 1 and site 2, respectively, which performed better than the height variability-based method (MR1994) with bias error of 20 percent and 48 percent at site 1 and site 2, respectively. The RA1994 model resulted in a larger range of Z 0 m than the MR1994 method. We also found that the mean, median and 75th percentiles of heights (H75) from ALS provides the best Z 0 m estimates in the MR1994 model, while the mean, median, and MLD (Median Absolute Deviation from Median Height), as well as AAD (Mean Absolute Deviation from Mean Height) heights from ALS provides the best Z 0 m estimates in the RA1994 model. In addition, the fractional cover of shrub and grass, distinguished with ALS and imaging spectroscopy data, provided the opportunity to estimate the frontal area index at the pixel-level to assess the influence of grass and shrub on Z 0 m estimates in the RA1994 method. Results indicate that grass had little effect on Z 0 m in the RA1994 method. The Z 0 m estimations were tightly coupled with vegetation height and its local variance for the shrubs. Overall, the results demonstrate that the use of height and fractional cover from remote sensing data are promising for estimating Z 0 m , and thus refining land surface models at regional scales in semiarid shrublands.

Photogrammetric Engineering and Remote Sensing↗

USGS2016

Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass

The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification. In this study, we explore the strengths of Landsat 8's Operational Land Imager (OLI) in estimating vegetation structure in a dryland ecosystem, and compare these results to Landsat 5's Thematic Mapper (TM). We also demonstrate the potential of OLI when coupled with light detection and ranging (lidar) in estimating vegetation cover and biomass in a dryland ecosystem. The OLI and TM predictions were similarly positive, indicating data from these sensors may be used in tandem for long-term time-series analysis. Results indicate shrub and herbaceous cover are well predicted with multi-temporal OLI data, and a combination of OLI and lidar derivatives improves most of these estimates and reduces uncertainty. For example, significant improvements were made for shrub cover (R 2 = 0.64 and 0.78 using OLI only and both OLI and lidar data, respectively). Importantly, a time series of OLI, with some improvement from lidar, provides strong estimates of herbaceous cover (68% of the variance is explained with OLI alone). In contrast, OLI data explain roughly 59% of the variance in total shrub biomass, however approximately 71% of the variance is explained when combined with lidar derivatives. To estimate the potential synergies of OLI and ICESat-2 we used simulated ICESat-2 photon data to predict vegetation structure. In a shrubland environment with a vegetation mean height of 1 m and mean vegetation cover of 33%, vegetation photons are able to explain nearly 50% of the variance in vegetation height. These results, and those from a comparison site, suggest that a lower detection threshold of ICESat-2 may be in the range of 30% canopy cover and roughly 1 m height in comparable dryland environments and these detection thresholds could be used to combine future ICESat-2 photon data with OLI spectral data for improved vegetation structure. Overall, the synergistic use of Landsat 8 and ICESat-2 may improve estimates of above-ground biomass and carbon storage in drylands that meet these minimum thresholds, increasing our ability to monitor drylands for fuel loading and the potential to sequester carbon.

Remote Sensing of Environment↗