A simple method for estimating the coarse lateral root biomass of shrubs using ground-penetrating radar: Validation by Caragana microphylla Lam. in Inner Mongolia.

The potential increases in carbon stocks in arid regions due to recent shrub encroachment have attracted extensive interest among both ecologists and carbon policy analysts. Quantifying the shrub root biomass amount in these ecosystems is essential to understanding the ecological changes occurring. In this paper, we proposed a simple nondestructive method for estimating the coarse lateral root biomass of shrubs based on the root counts obtained from ground-penetrating radar (GPR) radargrams. Root data were gathered via field experiments using GPR with antenna center frequencies of 900 MHz and 400 MHz. Five Caragana microphylla Lam. shrubs of different sizes were selected for measuring objects, and a total of 40 GPR survey lines were established for GPR data acquisition. The soil profile wall excavation method was used to obtain the total root biomass from each radargram. A model for estimating the root biomass was built by establishing the relationship between the root biomass in each profile and the root counts interpreted from the radargrams. According to the mathematical relationship between the root diameter and root biomass, the proxy root radius was derived, which could explain the rationality of the proposed model from the biological mechanism. The established model provided high confidence in estimating the root dry biomass using the GPR data obtained at the two antenna frequencies (R2= 0.73 for 900 MHz and R2= 0.71 for 400 MHz). The leave-one-out cross-validation results showed that the model exhibits satisfactory performance. This study expands the application of geophysical methods in root research and offers a new simplified method for estimating the root biomass from GPR data under field conditions.

A remote sensing method for mapping alpine grasslines based on graph-cut.

Climate change has induced substantial shifts in vegetation boundaries such as alpine treelines and shrublines, with widespread ecological and climatic influences. However, spatial and temporal changes in the upper elevational limit of alpine grasslands ("alpine grasslines") are still poorly understood due to lack of field observations and remote sensing estimates. In this study, taking the Tibetan Plateau as an example, we propose a novel method for automatically identifying alpine grasslines from multi-source remote sensing data and determining their positions at 30-m spatial resolution. We first identified 2895 mountains potentially having alpine grasslines. On each mountain, we identified a narrow area around the upper elevational limit of alpine grasslands where the alpine grassline was potentially located. Then, we used linear discriminant analysis to adaptively generate from Landsat reflectance features a synthetic feature that maximized the difference between vegetated and unvegetated pixels in each of these areas. After that, we designed a graph-cut algorithm to integrate the advantages of the Otsu and Canny approaches, which was used to determine the precise position of the alpine grassline from the synthetic feature image. Validation against alpine grasslines visually interpreted from a large number of high-spatial-resolution images showed a high level of accuracy (R2 , .99 and .98; mean absolute error, 22.6 and 36.2 m, vs. drone and PlanetScope images, respectively). Across the Tibetan Plateau, the alpine grassline elevation ranged from 4038 to 5380 m (5th-95th percentile), lower in the northeast and southeast and higher in the southwest. This study provides a method for remotely sensing alpine grasslines for the first-time at large scale and lays a foundation for investigating their responses to climate change.

A consistent and corrected nighttime light dataset (CCNL 1992-2013) from DMSP-OLS data.

Remote sensing of nighttime light can observe the artificial lights at night on the planet's surface. The Defense Meteorological Satellite Program's Operational Line Scan (DMSP-OLS) data (1992-2013) provide planet-scale nighttime light data over a long-time span and have been widely used in areas such as urbanization monitoring, socio-economic parameters estimation, and disaster assessment. However, due to the lack of an on-board calibration system, sensor design defects, limited light detection range, and inadequate quantization levels, the applications of DMSP-OLS data are greatly limited by interannual inconsistency, saturation, and blooming problems. To address these issues, we used the power function model based on pseudo-invariant feature, the saturation correction method based on regression model and radiance-calibrated data (SARMRC), and the self-adjusting model (SEAM) to improve the quality of DMSP data, and generated a Consistent and Corrected Nighttime Light dataset (CCNL 1992-2013). CCNL dataset shows good performance in interannual consistency, spatial details of urban centers, and light blooming, which is helpful to fully explore the application potentials of long time series nighttime light data.