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Abstract:Understanding the relationship between land use and surface water quality is necessary for effective water management. We estimated the impacts of catchment-wide land use on water quality during the dry and rainy seasons in the Dongjiang River basin, using remote sensing, geographic information systems and multivariate statistical techniques. The results showed that the 83 sites can be divided into three groups representing different land use types: forest, agriculture and urban. Water quality parameters exhibited significant variations between the urban-dominated and forest-dominated sites. The proportion of forested land was positively associated with dissolved oxygen concentration but negatively associated with water temperature, electrical conductivity, permanganate index, total phosphorus, total nitrogen, ammonia nitrogen, nitrate nitrogen and chlorophyll-a. The proportion of urban land was strongly positively associated with total nitrogen and ammonia nitrogen concentrations. Forested and urban land use had stronger impacts on water quality in the dry season than in the rainy OPEN ACCESSWater 2015, 7 4428 season. However, agricultural land use did not have a significant impact on water quality. Our study indicates that urban land use was the key factor affecting water quality change, and limiting point-source waste discharge in urban areas during the dry season would be critical for improving water quality in the study area.

The aim of this study was to assess the impacts of different land use practices on physicochemical characteristics and macroinvertebrate functional feeding groups (FFGs) in the Dongjiang River basin, southeast China and also to evaluate if macroinvertebrate FFGs match the river continuum concept (RCC) predictions. For this aim, a total of 30 sampling sites were selected that comprised three different land use types (10 forest, 10 agricultural, and 10 urban sites) and sampled during the dry season in January 2013. Analysis of variance results showed evident differences in the environmental factors among the three land use types, particularly between the forest and urban sites. The forest sites had significantly lower water temperature, lower stream order, higher pH, higher dissolved oxygen, higher elevation, and coarser substrates than the other land use sites. Conversely, the urban sites showed significantly higher mean values for electrical conductivity, nitrogen and phosphorus compounds. Significant differences in the shredder and predator richness and density were observed among land uses with more shredders and predators found in forest sites. Redundancy analysis showed a clear separation of forest sites from riparian modified areas (agriculture and urban use sites). Our results were broadly in accordance with the central RCC theme. However, the longitudinal distribution of predators and collectors did not completely match the prediction of the RCC. These results confirm that macroinvertebrate FFG structure has been altered by agricultural and urbanization practices in the Dongjiang River basin. Moreover, shredders and predators could be potential candidates for monitoring and assessing land use impacts on water quality in this basin to improve future watershed management.

The tobacco in plateau mountains has the characteristics of fragmented planting, uneven growth, and mixed/interplanting of crops. It is difficult to extract effective features using an object-oriented image analysis method to accurately extract tobacco planting areas. To this end, the advantage of deep learning features self-learning is relied on in this paper. An accurate extraction method of tobacco planting areas based on a deep semantic segmentation model from the unmanned aerial vehicle (UAV) remote sensing images in plateau mountains is proposed in this paper. Firstly, the tobacco semantic segmentation dataset is established using Labelme. Four deep semantic segmentation models of DeeplabV3+, PSPNet, SegNet, and U-Net are used to train the sample data in the dataset. Among them, in order to reduce the model training time, the MobileNet series of lightweight networks are used to replace the original backbone networks of the four network models. Finally, the predictive images are semantically segmented by trained networks, and the mean Intersection over Union (mIoU) is used to evaluate the accuracy. The experimental results show that, using DeeplabV3+, PSPNet, SegNet, and U-Net to perform semantic segmentation on 71 scene prediction images, the mIoU obtained is 0.9436, 0.9118, 0.9392, and 0.9473, respectively, and the accuracy of semantic segmentation is high. The feasibility of the deep semantic segmentation method for extracting tobacco planting surface from UAV remote sensing images has been verified, and the research method can provide a reference for subsequent automatic extraction of tobacco planting areas.

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