Starting in 1999, the Grain‐for‐Green Programme has been implemented in the Loess Plateau to alleviate the severe soil erosion by converting steeply sloping croplands to forestlands or grasslands. To quantify the effects of these conservation efforts, this study identified the land‐use changes between 2000 and 2015 and quantified their impacts on runoff and erosion using the Soil and Water Assessment Tools (SWAT) and a typical hilly basin, the Yanhe River basin as a case‐study. To heighten the applicability of SWAT to the region, major model parameters were localized and calibrated for the period of 1975–1980 and were then validated for 1981–1987. The R2 and NS validation indices were 0.70 and 0.65 for the monthly runoff and 0.67 and 0.61 for the sediment load, indicating that the model performance was acceptable. Between 2000 and 2015, the slope croplands were reduced by 39.9%, the forestlands increased by 90.2%, and the grasslands increased by 12.9%. These land‐use changes were simulated using SWAT to reduce the basin runoff by 13.8% and the sediment load by 50.7%. Spatial analyses using ArcGIS indicated that the simulated reduction in water yield due to cropland conversion to forestland was more obvious than that due to the conversion to grassland, but the reductions in the sediment yields were similar. The results suggest that the Grain‐for‐Green practice during this period was effective for preventing soil and water losses.

Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U2-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A–D) in the study area were selected to evaluate the performance of the U2-Net model in olive crown extraction in different scenarios, and the U2-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U2-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U2-Net model increased by 14.03–23.97 percentage points, 7.57–12.85 percentage points, and 8.15–14.78 percentage points, respectively. In addition, the U2-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R2) higher than 0.93 in all four subareas, suggesting that the U2-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U2-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees.

Changes in the climate and landcover are the two most important factors that influence terrestrial hydrological systems. Today, watershed-scale hydrological models are widely used to estimate the individual impacts of changes in the climate and landcover on watershed hydrology. The Minjiang river watershed is an ecologically and economically important, humid, subtropical watershed, located in south-eastern China. Several studies are available on the impacts of recent climate change on the watershed; however, no efforts have been made to separate the individual contributions of climate and landcover changes. This study is an attempt to separate the individual impacts of recent (1989–2018) climate and landcover changes on some of the important hydrological components of the watershed, and highlight the most influential changes in climate parameters and landcover classes. A calibrated soil and water assessment tool (SWAT) was employed for the study. The outcomes revealed that, during the study period, water yield decreased by 6.76%, while evapotranspiration, surface runoff and sediment yield increased by 1.08%, 24.11% and 33.85% respectively. The relative contribution of climate change to landcover change for the decrease in the water yield was 95%, while its contribution to the increases in evapotranspiration, surface runoff and sediment yield was 56%, 77% and 51%, respectively. The changes in climate parameters that were most likely responsible for changes in ET were increasing solar radiation and temperature and decreasing wind speed, those for changes in the water yield were decreasing autumn precipitation and increasing solar radiation and temperature, those for the increase in surface runoff were increasing summer and one-day maximum precipitation, while those for the increasing sediment yield were increasing winter and one-day maximum precipitation. Similarly, an increase in the croplands at the expense of needle-leaved forests was the landcover change that was most likely responsible for a decrease in the water yield and an increase in ET and sediment yield, while an increase in the amount of urban land at the expense of broadleaved forests and wetlands was the landcover change that was most likely responsible for increasing surface runoff. The findings of the study can provide support for improving management and protection of the watershed in the context of landcover and climate change.