Estimate Forest Aboveground Biomass of Mountain by ICESat-2/ATLAS Data Interacting Cokriging

Compared with the previous full-waveform data, the new generation of ICESat-2/ATLAS (Advanced Terrain Laser Altimeter System) has a larger footprint overlap density and a smaller footprint area. This study used ATLAS data to estimate forest aboveground biomass (AGB) in a high-altitude, ecologically fragile area. The paper used ATLAS data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Then, we combined biomass data from 54 ground samples to obtain the estimated AGB of 74,873 footprints using a hyperparametric optimized random forest (RF) model. The total AGB was estimated by combining the best variance function model in geostatistics with the slope that is the covariates. The results showed that among the 50 index parameters and three topographic variables extracted based on ATLAS, six variables showed a significant correlation with AGB. They were, in order, number of canopy photons, Landsat percentage canopy, canopy photon rate, slope, number of photons, and apparent surface reflectance. The optimized random forest model was used to estimate the AGB within the footprints. The model accuracy was the coefficient of determination (R2) = 0.93, the root mean square error (RMSE) = 10.13 t/hm2, and the population estimation accuracy was 83.3%. The optimized model has a good estimation effect and can be used for footprint AGB estimation. The spatial structure analysis of the variance function of footprint AGB showed that the spherical model had the largest fitting accuracy (R2 = 0.65, the residual sum of squares (RSS) = 2.65 × 10−4), the nugget (C0) was 0.21, and the spatial structure ratio was 94.0%. It showed that the AGB of footprints had strong spatial correlation and could be interpolated by kriging. Finally, the slope in the topographic variables was selected as the co-interpolation variable, and cokriging spatial interpolation was performed. Furthermore, a continuous map of AGB spatial distribution was obtained, and the total AGB was 6.07 × 107 t. The spatial distribution of AGB showed the same trend as the distribution of forest stock. The absolute accuracy of the estimation was 82.6%, using the statistical value of the forest resource planning and design survey as a reference. The ATLAS data can improve the accuracy of AGB estimation in mountain forests

Estimation of Forest Canopy Cover by Combining ICESat-2/ATLAS Data and Geostatistical Method/Co-Kriging

Accurately estimating forest canopy cover (FCC) is challenging by using traditional remote sensing images at the regional level due to the spectral saturation phenomenon. In this study, to improve the estimation accuracy, a new method of FCC wall-to-wall mapping was suggested based on ice, cloud, and land elevation satellite/advanced topographic laser altimeter system (ATLAS) data. Specifically, one dataset of FCC's observations was combined with preprocessed ATLAS data and topographic factors to build a random forest regression (RFR) model. Moreover, the Co-Kriging method was used to generate spatially explicit values that are required by the RFR from the point data of ATLAS parameters, and then the wall-to-wall mapping of the FCC was conducted. The results showed that the RFR model had an accuracy of relative root-mean-square error (rRMSE) = 0.09 with a coefficient of determination (R2) = 0.91. The best-fit semivariogram models between primary variables and covariates were asr and TR (Model: Gaussian model, R2 = 0.94, the residual sum of squares (RSS) = 1.73 × 10−6), landsat_perc and NDVI (Model: spherical model, R2 = 0.46, RSS = 1.58 × 10−4), and photon_rate_can and slope (Model: exponential model, R2 = 0.77, RSS = 6.45 × 10−4), respectively. FCC validation result showed that the FCC's wall-to-wall mapping was in great agreement with the dataset-2 (R2 = 0.79; rRMSE = 0.11)

The Formation of Phytic Acid–Silane Films on Cold-Rolled Steel and Corrosion Resistance

In this work, phytic acid (PA) and 3-mercaptopropyltrimethoxysilane (MPTS) underwent a condensation process to produce a phytic acid–silane (abbreviated PAS) passivation solution. Additionally, it was applied to the surface of cold-rolled steel to create a composite phytic acid–silane film. The functional groups of the passivation solution were analyzed by Fourier transform infrared spectroscopy (FT-IR). The composite film was evaluated using an electrochemical workstation, scanning electron microscope (SEM), energy dispersive spectrometer (EDS), X-ray photoelectron spectroscopy (XPS) and pull-off test. These techniques allowed for the characterization of the film’s micromorphology, oxidation, chemical composition and adhesion strength. The results show that the PAS composite film provides higher protection efficiency compared to cold-rolled steel substrates, low phosphorus passivation films, single phytate passivation films and commercial phosphate films. This composite film also has a higher adhesion strength, which is beneficial for subsequent coating, and a possible corrosion resistance mechanism was proposed as well. The PAS layer successfully prevents the penetration of corrosive media into the cold-rolled steel surface utilizing P–O–Fe bonds, thus improving the corrosion barrier effect of the substrate