The Mu Us Sandy Land is a key region of man-made desert control and farmland to forest (grass) return in China. Despite global change and the strong influence of human activities, the vegetation in this region has been significantly improved and restored. In this study, multisource remote sensing data and multiple indicators were used to quantitatively monitor and evaluate the vegetation restoration status in this area. The driving factors were also analysed. The results show that in the past 20 years, nearly the entire Mu Us Sandy Land significantly and substantively recovered. The regional fractional vegetation cover increased, with an average annual growth rate of 0.59% and obvious spatial heterogeneity. The nine most important driving factors could comprehensively account for 58.38% of the spatial distribution of the vegetation coverage. Factors such as land use and land cover, the aridity index, and gross domestic product had the most significant impact, followed by precipitation and temperature. The results confirmed that the vegetation was restored and improved in the Mu Us Sandy Land and determined the main driving factors, which is helpful for vegetation restoration and ecological improvement on sandy land similar to the Mu Us Sandy Land.

Dynamic changes in forest biomass are closely related to the carbon cycle, climate change, forest productivity and biodiversity. However, most previous studies mainly focused on the calculation of current forest biomass, and only a few studies attempted to predict future dynamic changes in forest biomass which obtained uncertain results. Therefore, this study comprehensively considered the effects of multi-stage continuous survey data of forest permanent sample plots, site condition factors and corresponding meteorological factors using Beijing as an example. The geographic detector method was used to screen the key interfering factors that affect the growth of forest biomass. Then, based on the back-propagation artificial neural network (BP-ANN) and support vector machine (SVM) learning methods, 80% of the sample data were extracted to train the model, and thereby verify the prediction accuracy of different modeling methods using different training samples. The results showed that the forest biomass prediction models based on both the machine learning algorithms had good fitting accuracy, and there was no significant difference in the prediction results between the two models. However, the SVM model was better than the BP-ANN. While the BP-ANN model provided more volatile predictions, and the accuracy was above 80%, the prediction results of the SVM model were relatively stable, and the accuracy was above 90%. This study not only provides good technical support for the scientific estimation of regional forest biomass in the future, but also offers reliable basic data for sustainable forest management, planning decisions, forest carbon sequestration and sustainable development.

The authors have studied the principal components of rainfall interception loss in a planted forest stand of Robinia pseudoacacia on the Loess Plateau. The purpose was to provide new information about the applicability of the original Gash analytical model to a new geographic location and to one of the primary species being used in the region’s reforestation program. The authors estimated forest structure parameters, including the mean evaporation rate, canopy storage capacity at saturation, free throughfall coefficient, rainfall fraction diverted to the trunks, and trunk storage capacity by using the intercepts and slopes obtained from regression analyses of the measured interception loss, throughfall, and stemflow versus gross rainfall. The interception and components of interception loss for trees in a Robinia pseudoacacia forest located on a south-facing slope were calculated using Gash’s analytical model. The total estimated interception loss during the period of observation was 10.8% greater...

The accurate habitat suitability evaluation of forest species is vital for forest resource management and conservation. Therefore, the previously published thresholds of soil organic carbon (SOC) contents for the six main forest species were used to screen sample points in this study; the maximum entropy modeling (MaxEnt) was applied to predict the potential distribution of those species in Lvliang Mountain, Shanxi Province, China. The following results were derived: (1) the area under the curve (AUC) value of the MaxEnt model was 0.905, indicating the model results had high accuracy; (2) the main environmental factors affecting the woodlands were mean diurnal temperature range, solar radiation, population density and slope; (3) the model accurately depicted the most suitable areas for those species, namely Populus davidiana Dode (Malpighiales: Salicaceae), Betula platyphylla Sukaczev (Fagales: Betulaceae), Quercus wutaishanica Mayr (Fagales: Fagaceae), Platycladus orientalis (L.) Franco (Pinales: Cupressaceae), Larix gmelinii (Rupr.) Kuzen. (Pinales: Pinaceae) and Pinus tabuliformis Carrière (Pinales: Pinaceae). This study has improved the representativeness of the samples based on prior knowledge to enhance the biological meaning and accuracy of the prediction results. Its findings provide a theoretical basis for the forest resource protection, management measures alongside the reconstruction of low-yield and low-efficiency forests.

Loess tilled surface is the geographical unit for the quantitative study of the soil erosion process. Contour tillage has been proved to be the most effictive measure for soil and water conservation in the Chinese Loess Plateau. Studies on the spatial heterogeneity of loess tilled surfaces will contribute to the understanding of the mechanism of erosion evolution. For this, a laboratory experiment was performed on contour tilled sloping surfaces where the hydrological-erosive processes were simulated. At different erosion stages, point cloud measurements were made using a terrestrial laser scanning system (TLS); then the heterogeneity depending on detrending and directionality were analysed by both the semivariogram method and the rescaled range analysis method. Results showed that: (1) the fractal dimensions D_S and D_R were equivalent when determined from either the semivariogram method or the rescaled range analysis method, although the semivariogram method appears to be more reliable in identifying the specific stage of the erosion evolution process; (2) the contour tilled microtopographies had an anisotropy behavior depending on direction; (3) the fractal dimension (either D_S or D_R) in different erosion stages was less than 1.5, which indicates that the microtopography of the sloping surface exhibits characteristics of persistent fractional Brownian motion and positive spatial autocorrelation. Irrespective of tillage measure and slope percentage, the sloping surface can be regarded as having random roughness. The results reveals a quantitative relationship between microtopography and sloping erosion. Also, it may provide guidance for further studies regarding the spatial variability and heterogeneity of various tilled slopes on the microtopographic scale.