A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

Influence of different nitrate–N monitoring strategies on load estimation as a base for model calibration and evaluation

Model-based predictions of the impact of land management practices on nutrient loading require measured nutrient flux data for model calibration and evaluation. Consequently, uncertainties in the monitoring data resulting from sample collection and load estimation methods influence the calibration, and thus, the parameter settings that affect the modeling results. To investigate this influence, we compared three different time-based sampling strategies and four different load estimation methods for model calibration and compared the results. For our study, we used the river basin model Soil and Water Assessment Tool on the intensively managed loess-dominated Parthe watershed (315 km²) in Central Germany. The results show that nitrate–N load estimations differ considerably depending on sampling strategy, load estimation method, and period of interest. Within our study period, the annual nitrate–N load estimation values for the daily composite data set have the lowest ranges (between 9.8% and 15.7% maximum deviations related to the mean value of all applied methods). By contrast, annual estimation results for the submonthly and the monthly data set vary in greater ranges (between 24.9% and 67.7%). To show differences between the sampling strategies, we calculated the percentage deviation of mean load estimations of submonthly and monthly data sets as related to the mean estimation value of the composite data set. For nitrate–N, the maximum deviation is 64.5% for the submonthly data set in the year 2000. We used average monthly nitrate–N loads of the daily composite data set to calibrate the model to achieve satisfactory simulation results [Nash–Sutcliffe efficiency (NSE) 0.52]. Using the same parameter settings with submonthly and monthly data set, the NSE dropped to 0.42 and 0.31, respectively. Considering the different results from the monitoring strategy and the load estimation method, we recommend both the implementation of optimized monitoring programs and the use of multiple load estimation methods to improve water quality characterization and provide appropriate model calibration and evaluation data.     

Squaring the Circle? Combining Models, Indicators, Experts and End-Users in Integrated Land-Use Management Support Tools

The most important challenges faced in the field of integrated land-use management are (i) harmonizing and integrating different datasets, (ii) selecting appropriate indicators, (iii) fitting suitable models to adequate scales, and finally (iv) integrating data, indicators and models into systems that allow both a high level of participation and flexibility with the adaptation to a variety of questions and applications. The articles of this special issue “Squaring the Circle? Combining Models, Indicators, Experts and End-Users in Integrated Land-Use Management Support Tools” demonstrate the challenges that are related to this topic. The case studies present examples of such integrated systems in order to recommend best practices to support land-use management and to reveal existing shortcomings. As a conclusion, seven features of a successful applicable integrated land-use management support system are derived: (1) ability to deal with discontinuity in information and datasets, (2) contribution to solve the problem of indicator diversity, (3) structuring the decision-making process, (4) support of participation processes in generating decisions, (5) development, comparison and evaluation of land-use alternatives, (6) assessment of the efficiency and trade-offs of management options, and (7) assistance of stakeholders in group communication processes.     

Environmental fate of amitrole: influence of dissolved organic matter

In this study the environmental fate of amitrole in terrestrial and aquatic model ecosystems was investigated. Under aerobic conditions mineralization of amitrole is the main degradation pathway. The experiments revealed that the leaching behaviour is low in the presence or the absence of dissolved organic matter (DOM) despite the high water solubility due to a strong binding of amitrole to soil constituents. Under anaerobic conditions the addition of DOM increases the transport of amitrole in soil columns. The tests with water/sediment model ecosystems showed that the mineralization of amitrole is lower in comparison to aerobic soil experiments. Up to 80.6% of the applied 14C-labelled amitrole transfer into the sediment and about 1/3 of this amount formed bound residues, which are not extractable.     

Introduction to SWAT+, A Completely Restructured Version of the Soil and Water Assessment Tool

SWAT+ is a completely restructured version of the Soil and Water Assessment Tool (SWAT) that was developed to face present and future challenges in water resources modeling and management and to meet the needs of the worldwide user community. It is expected to improve code development and maintenance; support data availability, analysis, and visualization; and enhance the model's capabilities in terms of the spatial representation of elements and processes within watersheds. The most important change is the implementation of landscape units and flow and pollutant routing across the landscape. Also, SWAT+ offers more flexibility than SWAT in defining management schedules, routing constituents, and connecting managed flow systems to the natural stream network. To test the basic hydrologic function of SWAT+, it was applied to the Little River Experimental Watershed (Georgia) without enhanced overland routing and compared with previous models. SWAT+ gave similar results and inaccuracies as these models did for streamflow and water balance. Taking full advantage of the new capabilities of SWAT+ regarding watershed discretization and landscape and river interactions is expected to improve simulations in future studies. While many capabilities of SWAT have already been enhanced in SWAT+ and new capabilities have been added, the model will continue to evolve in response to advancements in scientific knowledge and the demands of the growing worldwide user community. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Nitrogen loads through baseflow, stormflow, and underflow to Rehoboth Bay, Delaware

A detailed study of water and nitrogen (N) discharge from a small, representative subwatershed of Rehoboth Bay, Delaware, was conducted to determine total N loads to the bay. The concentrations of ammonium (NH4(+)), nitrate + nitrite (NO3(-) + NO2(-)), and dissolved and particulate organic N were determined in baseflow and storm waters discharging from Bundicks Branch from October 1998 to April 2002. A novel hydrographic separation model that accounts for significant decreases in baseflow during storm events was developed to estimate N loads during unsampled storms. Nitrogen loads based on gauged flows alone (7100-19,100 kg/yr) significantly underestimated those based on land use-land cover (LULC) and estimated N export factors from different classes of LULC (32,000-40,600 kg/yr). However, when ungauged underflow and associated N loads were included in the total loads (25,500-33,800 kg/yr), there was much better agreement with LULC export models. This suggests that in permeable coastal plain sediments, underflow contributes significantly to N fluxes to estuarine receiving waters, particularly in drier years. Based on the similarity in LULC, N loads from the Bundicks Branch subwatershed were used to estimate upland loads to the entire Rehoboth Bay Watershed (259,000-316,000 kg/yr). These N loads from the watershed were much greater than those from direct atmospheric deposition (49,000-64,500 kg/yr) and from a local wastewater treatment plant (9700-13,700 kg/yr). While the watershed was the principal source of N at all times during the year, the relative contributions from the watershed, wastewater, and direct atmospheric deposition varied predictably with season.     

Systems model of carbon transformations in soil subsidence

A system model was designed to simulate the interaction of climatologic and biologic factors in the biological oxidation of organic soils in ordert to stimulate research on this diminishing resource and attendant water quality problems in the Florida Everglades area. This is a mass balance model in which carbon and nitrogen are conserved in the transformations. It was designed to test different management alternatives for slowing the rate of soil subsidence and to estimate their potential effect on water quality by simulating subsidence rates and the release rates of nitrates, nitrites, and organic acids to ground and surface waters.The biological submodel is structured about two major compartments — the total non-living carbon compartment and a living carbon compartment that includes the microbial biomass and functioning extracellular enzymes.Simulations of the model show the effects of temperature, organic composition, and water table height on carbon dioxide evolution from the soil.     

Monitoring dissolved organic carbon in surface and drinking waters

The presence of natural organic matter (NOM) strongly impacts drinking water treatment, water quality, and water behavior during distribution. Dissolved organic carbon (DOC) concentrations were determined daily over a 22 month period in river water before and after conventional drinking water treatment using an on-line total organic carbon (TOC) analyzer. Quantitative and qualitative variations in organic matter were related to precipitation and runoff, seasons and operating conditions. Following a rainfall event, DOC levels could increase by 3.5 fold over baseflow concentrations, while color, UV absorbance values and turbidity increased by a factor of 8, 12 and 300, respectively. Treated water DOC levels were closely related to the source water quality, with an average organic matter removal of 42% after treatment.