Enhanced methods are required for mapping the forest aboveground biomass (AGB) over a large area in Chinese forests. This study attempted to develop an improved approach to retrieving biomass by combining PALSAR (Phased Array type L-band Synthetic Aperture Radar) and WorldView-2 data. A total of 33 variables with potential correlations with forest biomass were extracted from the above data. However, these parameters had poor fits to the observed biomass. Accordingly, the synergies of several variables were explored to identify improved relationships with the AGB. Using principal component analysis and multivariate linear regression (MLR), the accuracies of the biomass estimates obtained using PALSAR and WorldView-2 data were improved to approximately 65% to 71%. In addition, using the additional dataset developed from the fusion of FBD (fine beam dual-polarization) and WorldView-2 data improved the performance to 79% with an RMSE (root mean square error) of 35.13 Mg/ha when using the MLR method. Moreover, a further improvement (R² = 0.89, relative RMSE = 17.08%) was obtained by combining all the variables mentioned above. For the purpose of comparison with MLR, a neural network approach was also used to estimate the biomass. However, this approach did not produce significant improvements in the AGB estimates. Consequently, the final MLR model was recommended to map the AGB of the study area. Finally, analyses of estimated error in distinguishing forest types and vertical structures suggested that the RMSE decreases gradually from broad-leaved to coniferous to mixed forest. In terms of different vertical structures (VS), VS3 has a high error because the forest lacks undergrowth trees, while VS4 forest, which has approximately the same amounts of stems in each of the three DBH (diameter at breast height) classes (DBH > 20, 10 ≤ DBH ≤ 20, and DBH < 10 cm), has the lowest RMSE. This study demonstrates that the combination of PALSAR and WorldView-2 data is a promising approach to improve biomass estimation.

Background: Soil organic carbon (SOC) is important for soil quality and fertility in forest ecosystems. Labile SOC fractions are sensitive to environmental changes, which reflect the impact of short-term internal and external management measures on the soil carbon pool. Organic mulching (OM) alters the soil environment and promotes plant growth. However, little is known about the responses of SOC fractions in rhizosphere or bulk soil to OM in urban forests and its correlation with carbon composition in plants. Methods: A one-year field experiment with four treatments (OM at 0, 5, 10, and 20 cm thicknesses) was conducted in a 15-year-old Ligustrum lucidum plantation. Changes in the SOC fractions in the rhizosphere and bulk soil; the carbon content in the plant fine roots, leaves, and organic mulch; and several soil physicochemical properties were measured. The relationships between SOC fractions and the measured variables were analysed. Results: The OM treatments had no significant effect on the SOC fractions, except for the dissolved organic carbon (DOC). OM promoted the movement of SOC to deeper soil because of the increased carbon content in fine roots of subsoil. There were significant correlations between DOC and microbial biomass carbon and SOC and easily oxidised organic carbon. The OM had a greater effect on organic carbon fractions in the bulk soil than in the rhizosphere. The thinnest (5 cm) mulching layers showed the most rapid carbon decomposition over time. The time after OM had the greatest effect on the SOC fractions, followed by soil layer. Conclusions: The frequent addition of small amounts of organic mulch increased SOC accumulation in the present study. OM is a potential management model to enhance soil organic matter storage for maintaining urban forest productivity.

Broussonetia papyrifera is an important native tree species in China with strong adaptability, wide distribution, and economic importance. Climate change is considered as the main threat to ecological processes and global biodiversity. Predicting the potential geographical distribution of B. papyrifera in future climate change scenarios will provide a scientific basis for ecological restoration in China. Principal component analysis (PCA) and Pearson correlation analysis were conducted to select the environmental variables. The distribution and changes in the potential suitable area for B. papyrifera were predicted using the maximum entropy (MaxEnt) model and the CIMP6 dataset from 2041 to 2060. The current highly suitable areas for B. papyrifera were mainly located in Guangdong (5.60×10 ⁴ km ²), Guangxi (4.39×10 ⁴ km ²), Taiwan (2.54×10 ⁴ km ²), and Hainan (2.17×10 ⁴ km ²) provinces. The mean temperature of the coldest quarter (11.54-27.11℃), precipitation of the driest quarter (51.48-818.40 mm), and precipitation of the wettest quarter (665.51-2302.60 mm) were the main factors limiting the suitable areas for B. papyrifera. The multi-modal average of the highly and the total suitable areas for B. papyrifera were 111.42×10 ⁴ km ² and 349.11×10 ⁴ km ² in the SSP5-8.5 scenario, while those in the SSP1-2.6 scenario were 87.50×10 ⁴ km ² and 328.29×10 ⁴ km ², respectively. The gained suitable areas for B. papyrifera will expand to the western and northern China in the future scenarios. The multi-model averaging results showed that the potential available planting area was 212.66×10 ⁴ km ² and 229.32×10 ⁴ km ² in the SSP1-2.6 and SSP5-8.5 scenarios, respectively, when the suitable area within the farmland range was excluded.

This study attempted to measure forest resources at the individual tree level using high-resolution images by combining GPS, RS, and Geographic Information System (GIS) technologies. The images were acquired by the WorldView-2 satellite with a resolution of 0.5 m in the panchromatic band and 2.0 m in the multispectral bands. Field data of 90 plots were used to verify the interpreted accuracy. The tops of trees in three groups, namely ≥10 cm, ≥15 cm, and ≥20 cm DBH (diameter at breast height), were extracted by the individual tree crown (ITC) approach using filters with moving windows of 3 × 3 pixels, 5 × 5 pixels and 7 × 7 pixels, respectively. In the study area, there were 1,203,970 trees of DBH over 10 cm, and the interpreted accuracy was 73.68 ± 15.14% averaged over the 90 plots. The numbers of the trees that were ≥15 cm and ≥20 cm DBH were 727,887 and 548,919, with an average accuracy of 68.74 ± 17.21% and 71.92 ± 18.03%, respectively. The pixel-based classification showed that the classified accuracies of the 16 classes obtained using the eight multispectral bands were higher than those obtained using only the four standard bands. The increments ranged from 0.1% for the water class to 17.0% for Metasequoia glyptostroboides, with an average value of 4.8% for the 16 classes. In addition, to overcome the “mixed pixels” problem, a crown-based supervised classification, which can improve the classified accuracy of both dominant species and smaller classes, was used for generating a thematic map of tree species. The improvements of the crown- to pixel-based classification ranged from −1.6% for the open forest class to 34.3% for Metasequoia glyptostroboides, with an average value of 20.3% for the 10 classes. All tree tops were then annotated with the species attributes from the map, and a tree count of different species indicated that the forest of Purple Mountain is mainly dominated by Quercus acutissima, Liquidambar formosana and Pinus massoniana. The findings from this study lead to the recommendation of using the crown-based instead of the pixel-based classification approach in classifying mixed forests.

Nitrogen is an essential component in forest ecosystem nutrient cycling. Nitrogen fractions, such as dissolved nitrogen, ammonium, nitrate, and microbial biomass nitrogen, are sensitive indicators of soil nitrogen pools which affect soil fertility and nutrient cycling. However, the responses of nitrogen fractions in forest soils to organic mulching are less well understood. The rhizosphere is an important micro-region that must be considered to better understand element cycling between plants and the soil. A field investigation was carried out on the effect of mulching soil in a 15-year-old Ligustrum lucidum urban plantation. Changes in total nitrogen and nitrogen fractions in rhizosphere and bulk soil in the topsoil (upper 20 cm) and in the subsoil (20–40 cm) were evaluated following different levels of mulching, in addition to nitrogen contents in fine roots, leaves, and organic mulch. The relationships between nitrogen fractions and other measured variables were analysed. Organic mulching had no significant effect on most nitrogen fractions except for the rhizosphere microbial biomass nitrogen (MBN), and the thinnest (5 cm) mulching layer showed greater effects than other treatments. Rhizosphere MBN was more sensitive to mulching compared to bulk soil, and was more affected by soil environmental changes. Season and soil depth had more pronounced effects on nitrogen fractions than mulching. Total nitrogen and dissolved nitrogen were correlated to soil phosphorus, whereas other nitrogen fractions were strongly affected by soil physical properties (temperature, water content, bulk density). Mulching also decreased leaf nitrogen content, which was more related to soil nitrogen fractions (except for MBN) than nitrogen contents in either fine roots or organic mulch. Frequent applications of small quantities of organic mulch contribute to nitrogen transformation and utilization in urban forests.