Search

Copyright of Agricultural Science & Technology is the property of Hunan Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forestland dynamics can affect the ecological security of a country and even the global environment, and therefore it is of great practical significance to understand the characteristics of temporal and spatial variations of forestland. Taking Jiangxi Province as the study area, this study first explored the driving mechanism of the natural environment and social economy on deforestation and afforestation using a simultaneous equation model. The results indicate that population size, topographic and geomorphologic factors, climate, and location play leading roles in influencing forestland density fluctuations. Specifically, the population size, economic development level, gross value of forestry production, climate conditions, and government policies are key influencing factors of afforestation. Deforestation is mainly influenced by agricultural population, non-agricultural economy, forestry production, forestry density, location, transportation, and climate. In addition, this study simulated the spatial distribution of land use and analyzed the spatial characteristics and variation trends of forestland area and quality under the Representative Concentration Pathways (RCPs) climate scenarios from 2010 to 2030 using the Conversion of Land Use and its Effects (CLUE) model. The results indicate that forestland declines under the Asia-Pacific integrated model (AIM) climate scenario. The environment tends to be heavily damaged under this kind of scenarios, and measures should be taken in order to protect the environment. Although the model for energy supply strategy alternatives and their general environmental impact (MESSAGE) scenario is to some extent better than the AIM scenario, destruction of the environment will still occur, and it is necessary to restrain deforestation and convert shrub land intoforestland or garden land. These results can provide significant information for environmental protection, forest resource exploitation, and utilization in the areas experiencing deforestation and afforestation. [ABSTRACT FROM AUTHOR]

This study applies the Dynamics of Land System (DLS) model to simulating the land cover under the designed scenarios and then analyzes the effects of land cover conversion on energy flux in the semiarid grassland area of China with the Weather Research and Forecasting (WRF) model. The results indicate that the grassland will show a steadily upgrowing trend under the coordinated environmental sustainability (CES) scenario. Compared to the CES scenario, the rate of increase in grassland cover is lower, while the rate of increase in urban land cover will be higher under the rapid economic growth (REG) scenario. Although the conversion from cropland to grassland will reduce the energy flux, the expansion of urban area and decreasing of forestry area will bring about more energy flux. As a whole, the energy flux of near surface will obviously not change under the CES scenario, and the climate therefore will not be possible to be influenced greatly by land cover change. The energy flux under the REG scenario is higher than that under the CES scenario. Those research conclusions can offer valuable information for the land use planning and climate change adaptation in the semiarid grassland area of China. [ABSTRACT FROM AUTHOR]

With the water and soil conservation forests of Robinia pseudoacacia in the Malian beach of Hongqi farm, Ji County, Shanxi province, as the research object, this study estimated the potential evapotranspiration in the open space outside the forest and at the heights of 3m, 6m, and 10min the forests with the climate data during 2011-2012 and the upgraded Penman-Monteith formula; then, this study explored the microclimate characteristics inside and outside the Robinia pseudoacacia forest and thereafter revealed the vertical variation rules of potential evapotranspiration of Robinia pseudoacacia. The results indicate that the air temperature, relative humidity, wind speed, and potential evapotranspiration at different heights above the ground surface showed similar changing trends, but with some variation during different periods. In addition, the weather also had impacts on the potential evapotranspiration. In April, July, and October, the change of potential evapotranspiration of the Robinia pseudoacacia forests showed a bimodal curve in the sunny days and rainy days, while it showed a single-peak curve with quite small fluctuation in the rainy days. However, it showed a single-peak pattern even in the sunny days in January, and it showed no fluctuation in the snowy days in January. [ABSTRACT FROM AUTHOR]

• Twelve machine learning algorithms are compared to construct a retrieval model. • The TN concentration is retrieved using the UAV Hyper Spectral Remote Sensing data. • Spatial distribution of TN concentration is retrieved with high resolution. • Providing scientific references for water quality monitoring and management. Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1–2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring and management in the water reservoir. [ABSTRACT FROM AUTHOR]