The synergy of patterns vs. processes at a community level: a successful mechanism underlying subtropical native forests along the urban riparian zone

Riparian zone possesses ecological position with biota differing from aquatic body and terrestrial lands, and plant-animal coevolution may be the main factor for the framework of riparian vegetation. In the current study, the riparian plant community patterns along the subtropical mountainous riparian belts of Chongqing, China, was proposed to be regulated by co-evolving with the avifauna through propagule-dispersal process. The results show that: 1) the forests’ species composition and vertical layers are dominated by native catkins of Moraceae species with adapting traits of small and numerous propagules to frugivorous bird species, revealing an evolutionary trend different from the one in the terrestrial plant climax communities in the subtropics, and which forms a biological base for the plant-bird co-evolution; 2) there are significant associations of plant-bird species clusters, i.e., four plant-bird co-evolution groups (PBs) were divided out according to the plant species’ dominance and growth form relating to the fruit-dispersing birds’ abundance; 3) the correlation intensity within PB ranks as PBⅠ>Ⅱ>Ⅳ>Ⅲ, indicating the PBⅠis the leading type of co-evolution mainly shaped by the dominant plant species; 4) the PB correlation may be a key node between patterns vs. process of a riparian ecosystem responsible for the native vegetation, or even the ecosystem health. The results theoretically contribute new evidence to plant-animal co-evolution interpreting the forests’ characters in riparian environments, and urban planner and managers may simulate the native forests for restoring a more stable riparian biota, a better functioning ecosystem in subtropical zone.

Characterization and assessment of water quality in mountainous sub-basins on the Manhuaçu River, MG

The objective of this chapter is to carry out an analysis of the water quality of six different sites in the Manhuaçu River basin and to propose measures for the saved sites. Changes in land cover occur and can cause degradation of the landscape. Hydrographic data selected by IBGE and IEDE level curves on a scale of 1: 50,000 were used and performed on MDEHC using Arc GIS software. Six different locations were collected in the field in the Manhuaçu River basin in February 2019. The records provided by SAAE Manhuaçu were also used. How the analyzes were evaluated and using a Water Quality Index (IQA) for the saved locations. As sub-basins Córrego Bom Jesus, Rio Manhuaçu and Rio São Luis lowered low values of dissolved oxygen with values of 0.0; 2.3 and 2.5 mg l-1, respectively. Córrego Bom Jesus had the lowest IQA value, followed by Rio Manhuaçu and Rio São Luis with values of 36.69; 44.92 and 47.84 values considered ruins. High levels of pesticides were not detected in surface waters. The product of coffee plantations close to water courses and a potential contamination by pesticides are of concern, and it is necessary to increase a protected area in the riparian zone.

Combined Effects of Hillslope-Concentrated Flows and Riverine Stream Waves on Soil Erosion in the Reservoir Riparian Zone

During the exposed season, the water level fluctuation zone of the Three Gorges Reservoir has suffered from hillslope-concentrated flows and riverine stream waves, which considerably complicates the processes and magnifies the rate of bank erosion. This study depicts the forms and patterns of integrated bank erosion in this reservoir marginal landscape, decouples the evolutionary processes involved, explores the underlying mechanisms, and quantifies the magnitude through a case study on a fine-grained sandy bank. Hillslope-concentrated flows over rainfall storm events developed continuous gullies starting from uplands and extending throughout the entire slope of the reservoir bank, characterized by relatively larger depths and widths compared with discontinuous gullies on the lower slope developed by riverine stream waves.

Satellite-based analysis of classification algorithms applied to the riparian zone of the Malaya Kokshaga river

The paper comparatively analyses the accuracy of land cover classification in the riparian zone of the Malaya Kokshaga river in the Mari El Republic of Russia using Sentinel-2A satellite images with the algorithms of supervised classification: Maximum Likelihood (ML), Decision Tree (DT) and Neural Net (NN) in the ENVI-5.2 software package. Six main classes of land cover were identified based on field studies: coniferous, mixed (deciduous), shrublands, herbaceous, and water. The assessment of the area and the structure of land cover showed that forest covers 76% of the entire territory of the riparian area of the Malaya Kokshaga river. The analysis of the results of thematic mapping shows that the overall classification accuracy obtained by the ML algorithm is 96.09%, by NN - 94.51%, and by DT - 86.54%. The producer’s accuracy and user’s accuracy for most classes have the maximum value when the ML algorithm is used. For the NN algorithm, the maximum value of producer’s accuracy is observed for the mixed (deciduous) class, while for the DT algorithm – for the coniferous. When classified using all three algorithms the water and bare land classes were mixed, which requires more detailed work when estimating riparian forest ecosystems.

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Riparian vegetation delineation includes both the process of delineating the riparian zone and classifying vegetation within that zone. We developed a holistic framework to assess riparian vegetation delineation that includes evaluating channel boundary delineation accuracy using a combination of pixel- and object-based metrics. We also identified how stream order, riparian zone width, riparian land use, and image shadow influenced the accuracy of delineation and classification. We tested the framework by evaluating vegetation vs. non-vegetation riparian zone maps produced by applying random forest classification to aerial photographs with a 1 m pixel size. We assessed accuracy of the riparian vegetation classification and channel boundary delineation for two rivers in the northeastern United States. Overall accuracy for the channel boundary delineation was generally above 80% for both sites, while object-based accuracy revealed that 50% of delineated channel was less than 5 m away from the reference channel. Stream order affected channel boundary delineation accuracy while land use and image shadows influenced riparian vegetation classification accuracy; riparian zone width had little impact on observed accuracy. The holistic approach to quantification of accuracy that considers both channel boundary delineation and vegetation classification developed in this study provides an important tool to inform riparian management.