Modeling sub-boreal forest canopy bulk density in Minnesota, USA, using synthetic aperture radar and optical satellite sensor data

Background National estimates of canopy bulk density (CBD; kg m−3) for fire behavior modeling are generated and supported by the LANDFIRE program. However, locally derived estimates of CBD at finer scales are preferred over national estimates if they exist, as the absolute accuracy of the LANDFIRE CBD product is low and varies regionally. Active sensors (e.g., lidar or radar) are better suited for this task, as passive sensors are ill equipped to detect differences among key vertical fuel structures, such as coniferous surface fuels (≤2 m high) and canopy fuels above this threshold—a key categorical fuel distinction in fire behavior modeling. However, previous efforts to map CBD using lidar sensor data in the Superior National Forest (SNF) of Minnesota, USA, yielded substandard results. Therefore, we use a combination of dormant-season synthetic aperture radar (SAR) and optical satellite sensor data to (1) expand detectability of coniferous fuels among mixed forest canopies to improve the accuracy of CBD modeling and (2) better understand the influence of surface fuels in this regard. Response variables included FuelCalc output and indirect estimates of maximum burnable fuel based on canopy gap fraction (CGF) measured at ground level and 2 m above ground level. Results SAR variables were important predictors of CBD and total fuel density (TFD) in all independent model calibrations with ground data, in which we define TFD as the sum of CBD and primarily live coniferous surface fuel density (SFD) 0 to 2 m above ground. Exploratory estimates of TFD appeared biased to the presence of sapling-stage conifer fuel on measures of CGF at the ground level. Thus, modeling efforts to calibrate SFD with satellite sensor data failed. Both CGF-based and FuelCalc-based field estimates of CBD yielded close unity with satellite-calibrated estimates, although substantial differences in data distributions existed. Estimates of CBD from the widest CGF zenith angle range (0 to 38°) correlated best with FuelCalc-based CBD estimates, while both resulted in maximum biomass values that exceeded those considered typical for the SNF. Model results from the narrowest zenith angle range (0 to 7°) produced estimates of CBD that were more in line with values considered typical. LANDFIRE’s estimates of CBD were weakly, but significantly (P = 0.05), correlated to both narrow- and wide-angle CGF-based estimates of CBD, but not with FuelCalc-based estimates. Conclusions The combined use of field estimates of CBD, based on indirect measures of CGF according to Keane et al. (Canadian Journal of Forest Research 35:724–739, 2005), with SAR and optical satellite sensor data demonstrates the potential of this method for mapping CBD in the Upper Midwest, USA. Results suggested that the presence of live, coniferous surface fuels neither confounds remote detection nor precludes mapping of CBD in this region using SAR satellite sensor data, as C- and L-band idiosyncrasies likely limit the visibility of these smaller understory fuels from space. Nevertheless, research using direct measures of burnable SFD for calibrations with SAR satellite sensor data should be conducted to more definitively answer this remote detection question, as we suspect substantial bias among measures of CGF from ground level when estimating SFD as the difference between TFD and CBD.

Satellite Sensor EPIC Detects Aerosols in Earth’s Atmosphere

Aerosol observations from EPIC—a sensor aboard a satellite—align well with ground- and aircraft-based data, including measurements of smoke plumes produced by recent megafires.

Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations

Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat: R2 = 0.62 and RMSE = 77.10 μg cm−2; MERIS satellite data for soybeans: R2 = 0.24 and RMSE = 136.54 μg cm−2). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVI2 and random forest regression, with R2 = 0.65 and RMSE = 37.76 μg cm−2 (field spectra data) and R2 = 0.78 and RMSE = 47.96 μg cm−2 (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data.