On Landscape Patterns in Typical Mountainous Counties Middle Reaches of the Yangtze River in China.

The landscape patterns of plantations (PT) are the results of human disturbances on local vegetation, and in turn, differ greatly from natural forests (NF), since the patterns strongly influence the natural circulation of material and energy. There is a need to understand the differences of landscape patterns between PT and NF, to establish a near natural afforestation strategy. This study chose three typical silvicultural counties in the middle reaches of the Yangtze River as the research areas and compared the landscape patterns of NF and PT, with other land use types (grassland, GL; cropland, CL; shrubland, SL; orchard, OR; built-up land, BUL; bare land, BL; and water bodies, WB). The results revealed that the areas of PT accounted for 7.67%, 12.05%, and 18.97% of three counties, bigger than GL, OC, BUL, BL, and WB, as one of main land use types. The landscape patterns of PT (mean patch size between 2.06 to 6.05 ha) were more fragmented than NF (mean patch size between 5.83 to 53.91 ha). NF areas increased along the relative altitude gradient, from 0 to 2500 m, while PT areas peaked from 100-1000 m. The higher the altitude, the more typical the zonal distribution of PT, the more aggregated the NF. NF had significant negative correlations with BL, BUL, CL, PT, GL, and OC, which suggest that human activities had seriously interfered with NF. Although PT as an ecological protection strategy was increasing, the landscape patterns of PT were obviously different from NF. This may affect the material energy flow in the ecological environment. The results in the present study have great implications in the other regions in China and the relevant parts of the world where natural forests were heavily disturbed.

On Change of Soil Moisture Distribution With Vegetation Reconstruction in Mu Us Sandy Land of China, With Newly Designed Lysimeter.

BACKGROUND: China's so-called Three North Shelterbelt Program (3NSP) has produced a vast area of lined forest reconstruction in the semi-arid regions. This study uses the lined rain-fed Pinus sylvestris var. mongolica (PSM) sand-fixing forest in the eastern part of Mu Us Sandy Land in Northwestern China as an example to investigate the ecohydrological process in this region. Rain gauges, newly designed lysimeters and soil moisture sensors are used to monitor precipitation, deep soil recharge (DSR) and soil water content, where DSR specifically refers to recharge that can reach a depth more than 200 cm and eventually replenish the underneath groundwater reservoir. RESULTS: This study shows that there are two obvious moisture recharge processes in an annual base for the PSM forest soil: a snowmelt-related recharge process in the spring and a precipitation-related recharge process in the summer. The recharge depth of the first process can reach 180 cm without DSR occurring (in 2018). The second process results in noticeable DSR in 2018. Specifically, the DSR values over 2016-2018 are 1, 0.2, and 1.2 mm, respectively. To reach the recharge depths of 20, 40, 80, 120, 160, and 200 cm, the required precipitation intensities have to be 2.6, 3.2, 3.4, 8.2, 8.2, and 13.2 mm/d, respectively. The annual evapotranspiration in the PSM forest is 466.94 mm in 2016, 324.60 mm in 2017, and 183.85 mm in 2018. CONCLUSION: This study concludes that under the current precipitation conditions (including both dry- and wet-years such as 2016-2018), water consumption of PSM somewhat equals to the precipitation amount, and PSM has evolved over years to regulate its evapotranspiration in response to annual precipitation fluctuations in Mu Us Sandy Land of China.

[Forest lighting fire forecasting for Daxing'anling Mountains based on MAXENT model].

Daxing'anling Mountains is one of the areas with the highest occurrence of forest lighting fire in Heilongjiang Province, and developing a lightning fire forecast model to accurately predict the forest fires in this area is of importance. Based on the data of forest lightning fires and environment variables, the MAXENT model was used to predict the lightning fire in Daxing' anling region. Firstly, we studied the collinear diagnostic of each environment variable, evaluated the importance of the environmental variables using training gain and the Jackknife method, and then evaluated the prediction accuracy of the MAXENT model using the max Kappa value and the AUC value. The results showed that the variance inflation factor (VIF) values of lightning energy and neutralized charge were 5.012 and 6.230, respectively. They were collinear with the other variables, so the model could not be used for training. Daily rainfall, the number of cloud-to-ground lightning, and current intensity of cloud-to-ground lightning were the three most important factors affecting the lightning fires in the forest, while the daily average wind speed and the slope was of less importance. With the increase of the proportion of test data, the max Kappa and AUC values were increased. The max Kappa values were above 0.75 and the average value was 0.772, while all of the AUC values were above 0.5 and the average value was 0. 859. With a moderate level of prediction accuracy being achieved, the MAXENT model could be used to predict forest lightning fire in Daxing'anling Mountains.