Forest stock volume (FSV) is a major indicator of forest ecosystem health and it also plays an important part in understanding the worldwide carbon cycle. A precise comprehension of the distribution patterns and variations of FSV is crucial in the assessment of the sequestration potential of forest carbon and optimization of the management programs of the forest carbon sink. In this study, a novel vegetation index based on Sentinel-2 data for modeling FSV with the random forest (RF) algorithm in Helan Mountains, China has been developed. Among all the other variables and with a correlation coefficient of r = 0.778, the novel vegetation index (NDVI_RE) developed based on the red-edge bands of the Sentinel-2 data was the most significant. Meanwhile, the model that combined bands and vegetation indices (bands + VIs-based model, BVBM) performed best in the training phase (R² = 0.93, RMSE = 10.82 m³ha⁻¹) and testing phase (R² = 0.60, RMSE = 27.05 m³ha⁻¹). Using the best training model, the FSV of the Helan Mountains was first mapped and an accuracy of 80.46% was obtained. The novel vegetation index developed based on the red-edge bands of the Sentinel-2 data and RF algorithm is thus the most effective method to assess the FSV. In addition, this method can provide a new method to estimate the FSV in other areas, especially in the management of forest carbon sequestration.

In this study, we investigated the accumulation and transport patterns of phthalate ester (PAEs) compounds in the soil-plant system of maize and cabbage fields under different film mulching years. The total content of five PAEs in the maize fields soil ranged from 156.19 to 566.1 μg kg⁻¹ with film mulching for 0–20 years. The total content of five PAEs in the cabbage field soils ranged from 252.48 to 559.07 μg kg⁻¹ with film mulching for 0–30 years. The PAEs content in cabbage and maize field soils was lower than the environmental quality standards for soil (GB 15618-2008). With the increase in film mulching years, both maize and cabbage soil PAEs contents increased significantly (p < 0.05), while among the five PAEs, the contents of di-n-butyl phthalate (DnBP) and di(2-ethylhexyl) phthalate (DEHP) increased the most significantly, and the content of DEHP in the soils was the highest. The DEHP content in soils planted with maize ranges from 134.03 to 406.79 μg kg⁻¹ with the film mulching for 20 years; while in soil planted with cabbage, it ranges from 229.35 to 405.2 μg kg⁻¹ with the film mulching for 30 years. The DEHP content in all of the soils has not exceed the limit value established by the USEPA in U.S. Five PAEs were detected in maize seeds and cabbage leaves, among which the DEHP content exceeded the maximum residue limit set by the National Food Safety Standard (GB 9685-2016) for DEHP in food after more than 10 years of mulching. The bioaccumulation and translocation factors in cabbage showed a significant positive correlation with the film mulching years. However, the bioaccumulation factor and translocation factor in maize have weak correlations with the film mulching years, possibly due to the lower PAEs content in the deep soil layers where maize roots are distributed. Our research revealed a significant association between the presence and distribution of PAEs in crops and mulching years. These novel discoveries provide fundamental information for controlling PAEs pollution in agricultural environments.

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. Due to the short and sparse features of shrubs, capturing the shrub biomass accurately by satellite observations is challenging. Previous studies mostly used ground samples and satellite observations to estimate shrub biomass by establishing a direct connection, which was often hindered by the limited number of ground samples and spatial scale mismatch between samples and observations. Unmanned aerial vehicles (UAVs) provide opportunities to obtain more samples that are in line with the aspects of satellite observations (i.e., scale) for regional-scale shrub biomass estimations accurately with low costs. However, few studies have been conducted based on the air-space-ground-scale connection assisted by UAVs. Here we developed a framework for estimating 10 m shrub biomass at a regional scale by integrating ground measurements, UAV, Landsat, and Sentinel-1/2 observations. First, the spatial distribution map of shrublands and non-shrublands was generated in 2023 in the Helan Mountains of Ningxia province, China. This map had an F1 score of 0.92. Subsequently, the UAV-based shrub biomass map was estimated using an empirical model between the biomass and the crown area of shrubs, which was aggregated at a 10 m × 10 m grid to match the spatial resolution of Sentinel-1/2 images. Then, a regional-scale estimation model of shrub biomass was developed with a random forest regression (RFR) approach driven by ground biomass measurements, UAV-based biomass, and the optimal satellite metrics. Finally, the developed model was used to produce the biomass map of shrublands over the study area in 2023. The uncertainty of the resultant biomass map was characterized by the pixel-level standard deviation (SD) using the leave-one-out cross-validation (LOOCV) method. The results suggested that the integration of multi-scale observations from the ground, UAVs, and satellites provided a promising approach to obtaining the regional shrub biomass accurately. Our developed model, which integrates satellite spectral bands and vegetation indices (R² = 0.62), outperformed models driven solely by spectral bands (R² = 0.33) or vegetation indices (R² = 0.55). In addition, our estimated biomass has an average uncertainty of less than 4%, with the lowest values (30%) and biomass production (>300 g/m²). This study provides a methodology to accurately monitor the shrub biomass from satellite images assisted by near-ground UAV observations as well as ground measurements.

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. However, capturing the shrub biomass accurately by satellite observations is challenging due to the short and sparse features of shrubs. In this study, we presented a framework to estimate shrub biomass at a 10m spatial resolution by integrating ground in-situ data, unmanned aerial vehicle (UAV), Sentinel-2, and Sentinel-1 observations. The pilot study was conducted in the Helan mountains of Ningxia province, China. First, the spatial distribution map of shrubland and other land cover types was generated in 2023. Then, a prediction model of shrub biomass was developed in a Random Forest Regression (RFR) approach driven by different predicted variable datasets based on in-situ measurements, UAV, and satellite images. Finally, the developed model was used to produce the biomass map of shrubland in 2023. The uncertainty was characterized by creating a standard deviation (SD) map using the Leave-One-Out Cross-Validation (LOOCV) method in the shrub biomass estimates. The resultant shrubland distribution map in 2023 has an F1 score of 0.92. The shrub biomass model driven by spectral bands and vegetation indices (R2= 0.62) demonstrated superior performance than that only driven by spectral bands (R2=0.33) or vegetation indices (R2=0.55). The uncertainty of the estimated biomass was lower than 4%, with the lowest values (30%) and biomass production (>30kg/m2) regions. Furthermore, the findings show that the magnitude of shrub biomass was affected by the aridity and precipitation significantly in the study region. This study provides a workflow to accurately monitor the biomass of shrublands in a complicated environment based on remote sensing images from multi-platform observations.

Forests are one of the most important ecosystems on Earth, playing a crucial role in maintaining global ecological balance and climate stability. Forest aboveground biomass (AGB) is a key component...