Evaluating nitrogen removal by vegetation uptake using satellite image time series in riparian catchments

Spatial optimization of ecological ditches for non-point source pollutants under urban growth scenarios

Non-point source (NPS) pollution is regarded as the major threat to water quality worldwide, and ecological ditches (EDs) are considered an important and widely used method to collect and move NPS pollutants from fields to downstream water bodies. However, few studies have been conducted to optimize the spatial locations of EDs, particularly when the watershed experiences urbanization and rapid land-use changes. As land-use patterns change the spatial distribution of NPS loads, this study used a cellular automata-Markov method to simulate future land-use changes in a typical agricultural watershed. Three scenarios are included as follows: historical trend, rapid urbanization, and ecological protection scenarios. The spatial distributions of particulate phosphorus loads were simulated using the revised universal soil loss equation and sediment transport distribution model. The results suggested that the total particulate phosphorus (TP) load in the Zhuxi watershed decreased by 10,555.2 kg from 2000 to 2020, primarily because the quality and quantity of forests in Zhuxi County improved over the last 20 years. The TP load in Zhuxi watershed would be 2588.49, 2639.15, and 2553.32 kg in 2040 in historical trend, rapid urbanization, and ecological protection scenarios, respectively, compared with 2308.1 kg in 2020. This indicated that urban expansion increases the TP load, and the faster the expansion rate, the more the TP load. Consequently, the optimal locations of EDs were determined based on the intercepted loads and the period during which they existed during land-use changes. The results suggested that rapid urbanization would consequently reduce the space available for building EDs and also increase the cost of building EDs to control the NPS pollution in the watershed.

Interannual variation in riparian vegetation cover and its relationship with river flow under a high level of human intervention: an example from the Yongding River Basin

Riparian vegetation cover is significantly affected by a river's hydrological conditions. Especially in arid and semiarid areas, low flow will degrade riparian vegetation, and recent, intensive human activities in the Yongding River Basin have caused a sharp decrease in river flow. We analyzed interannual change in riparian vegetation, river flow effects, and land use on vegetation coverage using the 40 years (1977-2016) of remote sensing images and river flow, combined with 38 years (1980-2018) of land use data. The normalized difference vegetation index (NDVI) was used to determine vegetation cover in five different categories: extremely low, low, medium, high, and extremely high based on the pixel dichotomy model. The weighted average was calculated to obtain vegetation cover trends. We show that riparian vegetation cover from four rivers increased. Compared with 1977, in 2016, combined high and extremely high vegetation covers at the Dongyang, Yang, Sanggan, and Yongding Rivers increased by 20.3%, 26.7%, 50.0%, and 39.2%, respectively. High (R =  -0.976, P < 0.01) and extremely high (R =  -0.762, P < 0.05) vegetation covers are negatively correlated with flow in the Yongding River. The high vegetation cover of the Sanggan River riparian zone is negatively correlated with river flow (R =  -0.683, P < 0.05). In the Dongyang and Sanggan Rivers, land use analysis in the riparian zone showed that change in cultivated land, grassland, and forest were significantly correlated with high and extremely high vegetation cover. The abundant cultivated land and restoration activities are likely responsible for the increase of riparian vegetation cover as river flows decline.

Vegetation Properties in Human-Impacted Riparian Zones Based on Unmanned Aerial Vehicle (UAV) Imagery: An Analysis of River Reaches in the Yongding River Basin

Riparian zones, transitional areas between aquatic and terrestrial ecosystems, have high plant species diversities. However, they are extremely vulnerable to natural factors, such as changes in river hydrological conditions (floods, droughts) and disturbances from human activities (dams, farmland encroachment, etc.). The distribution of plant life forms and variations in the degree of vegetation coverage in a riparian zone can reflect changes in the environmental conditions. In this study, we analyzed eight reaches from the four main tributaries (Dongyang River, Yang River, Sanggan River, and Yongding River) of the Yongding River Basin, which were selected based on their climate, terrain, and degree of human disturbance. One reach was located on the Dongyang River (DYR), two reaches on the Yang River (YR1 and YR2), three on the Sanggan River (SGR1, 2, and 3), and two on the Yongding River (YDR1 and YDR2). Unmanned aerial vehicle (UAV) technology was used to obtain high-resolution, true-color, multispectral images. The distributions of the plant life forms and the differences in the vegetation coverage were analyzed in the eight selected riparian zones. The results showed that grasses dominated the riparian zone and shrubs and trees were sparsely distributed along both banks of all streams, excluding SGR2 and YDR1. The areas with an extremely high vegetation coverage classification accounted for the highest proportion in the DYR (29.3%), YR2 (48.1%), SGR1 (32.9%), SGR2 (39.9%), SGR3 (85.1%), YDR1 (36.7%), and YDR2 (51.1%) reaches. Extremely low vegetation coverage accounted for the highest proportion in the YR2 reach, reaching 37.4%. This study indicated that natural factors and human activities have a serious impact on the distribution of different plants life forms and vegetation coverage classifications in the riparian zones of the Yongding River Basin. We hope that this research can provide practical assistance in the efforts of ecological restoration and the management of riparian vegetation in the Yongding River Basin.

Salmon abundance and patterns of forest greenness as measured by satellite imagery

Linkages across ecosystems can shape productivity. Salmon carcasses are exemplary of cross-system linkages, because they can fertilize riparian vegetation and shape patterns of terrestrial biodiversity. Detection of salmon fertilization effects has been confined to field-based studies that are limited in scale. Here we use satellite images to quantify the effects of salmon on greenness of riparian vegetation. We measure tree greenness across spatial and temporal gradients of salmon fertilization effects in two regions. In the first case study, we find that deciduous trees are greener in years following large salmon spawning events, and that the magnitude of this effect was related to the specific abundance of spawning salmon. In the second case study we compare greenness of mixed evergreen and deciduous forests across different watersheds that have different salmon spawning densities. We found greenness was related positively to salmon spawning density near streams with high evergreen cover and flat stream banks. These findings suggest that the effect of salmon carcasses on riparian vegetation may be detectable from space. Further work on this approach, especially with high spatial, temporal and spectral data, may allow estimation of the spatial extent of nutrient enrichment from salmon carcasses and aid ecosystem-based management to protect important ecosystem linkages.

Investigation of the impacts of urban vegetation loss on the ecosystem service of air pollution mitigation in Karaj metropolis, Iran

The present study aims to investigate the relationship between reduced air pollution and ecosystem services in Karaj metropolis, Iran. To the end, the trends in the concentrations of O₃, NO₂, CO, SO₂, PM₁₀, and PM_2.5 as the main atmospheric pollutants of Karaj were studied. Five time series models of autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) were used to predict changes in air pollutant concentrations. Air pollution zoning is conducted via ArcGIS_10.3 by using spline tension interpolation method. Then, normalized difference vegetation index (NDVI) was obtained from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to analyze vegetation dynamics as an index of ecosystem functioning. NDVI thresholds were selected to present guidelines for qualitative and quantitative changes in green cover and were divided into five different categories. Based on the results, AR (1) and ARIMA (1,2,1) were recognized as appropriate models for predicting the concentration of air pollutants in the study area. A decrease in very dense vegetation coverage and increase in poor vegetation areas, followed by an increase in air pollution, revealed that the loss of urban green coverage and decreased ecosystem services were positively related. Furthermore, the expansion of urban lands toward the north and the west from the baseline to future condition led to great changes in the land cover and losses in vegetation along these axes, which finally resulted in increased air pollution in these areas. Thus, the results of this study can be directly used in decision-making in the area of air pollution.

A framework for assessing the effects of afforestation and South-to-North Water Transfer on nitrogen and phosphorus uptake by plants in a critical riparian zone

The uptake of nitrogen (N) and phosphorus (P) by plants in riparian zones can significantly decrease the water pollution risk. Moreover, the vegetation area in riparian zone can be impacted by raising of water level and afforestation. As the largest reservoir in North China, the Miyun Reservoir is affected by the South-to-North Water Transfer (SNWT) and large-scale afforestation. However, few efficient technology frameworks that can be used to assess the effects of similar anthropogenic projections on N and P uptake by plants at riparian zone catchment scale have been reported. Therefore, this study proposed a framework including an ecological simulation tool coupled with multi-source data and scenario setting methods to identify the effects of these two projects on the uptake of N and P by plants in Miyun Reservoir riparian zone from April to September in 2015. The results show that the total N and P uptake by plants in Miyun Reservoir riparian zone are 1214.18 t and 148.66 t in growing seasons. After afforestation, the N (P) removal will increase by 2.56 (2.17) times in the impacted area (below 160 m in elevation). When the water level rises to 150 m in elevation, the joint effects of afforestation and SNWT will increase the total N and P removals by 851.18 t and 83.33 t. This implies that the afforestation can offset the negative effect on N (P) removal caused by SNWT. Overall, this study can provide useful scientific reference for the design and effective management of the riparian zone.

NDVI, scale invariance and the modifiable areal unit problem: An assessment of vegetation in the Adelaide Parklands

This research addresses the question as to whether or not the Normalised Difference Vegetation Index (NDVI) is scale invariant (i.e. constant over spatial aggregation) for pure pixels of urban vegetation. It has been long recognized that there are issues related to the modifiable areal unit problem (MAUP) pertaining to indices such as NDVI and images at varying spatial resolutions. These issues are relevant to using NDVI values in spatial analyses. We compare two different methods of calculation of a mean NDVI: 1) using pixel values of NDVI within feature/object boundaries and 2) first calculating the mean red and mean near-infrared across all feature pixels and then calculating NDVI. We explore the nature and magnitude of these differences for images taken from two sensors, a 1.24m resolution WorldView-3 and a 0.1m resolution digital aerial image. We apply these methods over an urban park located in the Adelaide Parklands of South Australia. We demonstrate that the MAUP is not an issue for calculation of NDVI within a sensor for pure urban vegetation pixels. This may prove useful for future rule-based monitoring of the ecosystem functioning of green infrastructure.

Detecting and analyzing soil phosphorus loss associated with critical source areas using a remote sensing approach

The detection of critical source areas (CSAs) is a key step in managing soil phosphorus (P) loss and preventing the long-term eutrophication of water bodies at regional scale. Most related studies, however, focus on a local scale, which prevents a clear understanding of the spatial distribution of CSAs for soil P loss at regional scale. Moreover, the continual, long-term variation in CSAs was scarcely reported. It is impossible to identify the factors driving the variation in CSAs, or to collect land surface information essential for CSAs detection, by merely using the conventional methodologies at regional scale. This study proposes a new regional-scale approach, based on three satellite sensors (ASTER, TM/ETM and MODIS), that were implemented successfully to detect CSAs at regional scale over 15years (2000-2014). The approach incorporated five factors (precipitation, slope, soil erosion, land use, soil total phosphorus) that drive soil P loss from CSAs. Results show that the average area of critical phosphorus source areas (CPSAs) was 15,056km² over the 15-year period, and it occupied 13.8% of the total area, with a range varying from 1.2% to 23.0%, in a representative, intensive agricultural area of China. In contrast to previous studies, we found that the locations of CSAs with P loss are spatially variable, and are more dispersed in their distribution over the long term. We also found that precipitation acts as a key driving factor in the variation of CSAs at regional scale. The regional-scale method can provide scientific guidance for managing soil phosphorus loss and preventing the long-term eutrophication of water bodies at regional scale, and shows great potential for exploring factors that drive the variation in CSAs at global scale.

A method coupled with remote sensing data to evaluate non-point source pollution in the Xin'anjiang catchment of China

Non-point source (NPS) pollution has been recognized as the largest threat to water resources throughout the world, and the evaluation of NPS loads is a priority. In China, some models, such as SWAT (Soil and Water Assessment Tools) model, have been widely used at the watershed scale. However, variations in natural and social factors make it difficult to find a proper model to use on NPS pollution management in China. In this study, a "Dualistic Structure" model is coupled with remote sensing data to capture the spatial and temporal processes of NPS pollution. Land parameters were derived from HJ-1A and HJ-1B satellite data (resolution 30 m), which offered greatly enhanced spatial resolution. This approach offers the advantage of being a rapid estimation system with fairly precise knowledge of the distribution, sources and quantities of NPS pollutants, and it can be used at the country scale, including in areas with insufficient data. The method is used in the Xin'anjiang catchment, an important water source for Hangzhou city, China. The simulation in this study includes the spatial distribution of monthly total nitrogen (TN), total phosphorous (TP), ammonia nitrogen (NH(4)-N) and chemical oxygen demand (COD(cr)) loads and the total production of NPS pollutants. The simulations were compared to pollution census (PC) data in 2010 and the results of SWAT model, with an average R(2) larger than 0.7. Additionally, the impacts of soil erosion and human activities on NPS pollution were assessed, indicating that soil and water conservation is very significant factor in the Xin'anjiang catchment. Results indicate that by coupling remote sensing data and parameter retrieval techniques to "Dualistic Structure" models, estimations of NPS loads on the catchment scale can be improved by spatial pixel-based modeling. This rapid NPS estimation system will offer effective support to policy makers for environmental management in China.