Monitoring and assessing of landscape heterogeneity at different scales

In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions.     

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.     

SOMPROF: A vertically explicit soil organic matter model

Most current soil organic matter (SOM) models represent the soil as a bulk without specification of the vertical distribution of SOM in the soil profile. However, the vertical SOM profile may be of great importance for soil carbon cycling, both on short (hours to years) time scale, due to interactions with the soil temperature and moisture profile, as well as on long (years to centuries) time scale because of depth-specific stabilization mechanisms of organic matter. It is likely that a representation of the SOM profile and surface organic layers in SOM models can improve predictions of the response of land surface fluxes to climate and environmental variability. Although models capable of simulating the vertical SOM profile exist, these were generally not developed for large scale predictive simulations and do not adequately represent surface organic horizons. We present SOMPROF, a vertically explicit SOM model, designed for implementation into large scale ecosystem and land surface models. The model dynamically simulates the vertical SOM profile and organic layer stocks based on mechanistic representations of bioturbation, liquid phase transport of organic matter, and vertical distribution of root litter input. We tested the model based on data from an old growth deciduous forest (Hainich) in Germany, and performed a sensitivity analysis of the transport parameters, and the effects of the vertical SOM distribution on temporal variation of heterotrophic respiration. Model results compare well with measured organic carbon profiles and stocks. SOMPROF is able to simulate a wide range of SOM profiles, using parameter values that are realistic compared to those found in previous studies. Results of the sensitivity analysis show that the vertical SOM distribution strongly affects temporal variation of heterotrophic respiration due to interactions with the soil temperature and moisture profile.     

Prosulfocarb at center stage!

Environmental Science and Pollution Research - Prosulfocarb is a thiocarbamate herbicide that is rapidly growing in use due to the progressive bioresistance of weeds to certain pesticides and the...     

Defining a successful superfund clean-up: A community representative's perspective

The successful use of the Superfund program involves developing partnerships with all of the stakeholders early in the process. Citizens living near Superfund sites are the primary stakeholders, falling victim to many health risks and economic costs. When equipped with technical advisors, citizens can play a primary role in the remediation decisions being made at hazardous waste sites. This article illustrates the important role and impact of concerned citizens living near the Niagara Mohawk Power Corporation Superfund site in Saratoga Springs, New York. The Superfund program was used successfully at this site in a number of ways, mainly in that it provided technical advisors through a technical assistance grant (TAG) for the citizens, which resulted in a thorough and conclusive remedial investigation.     

Siblicide in Serengeti spotted hyenas: a long-term study of maternal input and cub survival

In the Serengeti National Park, Tanzania, large fluctuations of prey abundance alters the frequency at which spotted hyena (Crocuta crocuta) cubs are nursed and thus the total level of maternal input available to them. Maternal input is high when mothers feed on high densities of locally available migratory herbivores and low when mothers travel up to 70 km to forage. Using data from 19 cub cohorts on the incidence of siblicide (from monitoring the survival of 609 cubs in twin litters) and cub growth rates (from 195 cubs in twin litters) as a measure of maternal input, we demonstrate that the incidence of siblicide increased as average cohort growth rate declined. In total, there were 37 siblicides in 384 litters (9% of litters). When both cubs were alive, total maternal input in siblicidal litters was significantly lower than in non-siblicidal litters and the mean share of the dominant sib of 64.6% was significantly higher than the mean of 52.1% for dominant sibs of non-siblicidal litters. After siblicide, growth rates of siblicide victors substantially increased, demonstrating that mothers did not reduce maternal input after litter reduction. As a result, siblicide victors achieved a long-term growth rate similar to that of singletons and thus significantly increased their expected survival. We conclude that in spotted hyenas, high maternal input in lactation has favoured the evolution of facultative siblicide in populations inhabiting areas with low or fluctuating food resources.     

Assessment of the genotoxicity of 137Cs radiation using Vicia-micronucleus, Tradescantia-micronucleus and Tradescantia-stamen-hair mutation bioassays

Since the middle of the 20th century, ionizing radiations from radioactive isotopes including 137Cs have been investigated to determine their genotoxic impact on living organisms. The present study was designed to compare the effectiveness of three plant bioassays to assess DNA damage induced by low doses of 137Cs: Vicia-micronucleus test (Vicia-MCN), Tradescantia-micronucleus test (Trad-MCN) and Tradescantia-stamen-hair mutation test (Trad-SH) were used. Vicia faba (broad bean) and Tradescantia clone 4430 (spiderwort) were exposed to 137Cs according to different scenarios: external and internal (contamination) irradiations. Experiments were conducted with various levels of radioactivity in solution or in soil, using solid or liquid 137Cs sources. The three bioassays showed different sensitivities to the treatments. Trad-MCN appeared to be the most sensitive test (significative response from 1.5 kBq/200 ml after 30 h of contamination). Moreover, at comparable doses, internal irradiations led to larger effects for the three bioassays. These bioassays are effective tests for assessing the genotoxic effects of radioactive 137Cs pollution.