Small scale variation at Bavarian soil monitoring sites : a contribution to estimate the uncertainty of the German Level-I Monitoring of soils (BZE II)

Anhand der Daten von 33 Standorten des Bayerischen Waldboden Dauerbeobachtungsprogramms (Bodendauerbeobachtungsflächen, BDF) wurden die durch kleinräumige Variabilität verursachten Unsicherheiten bei der Charakterisierung von Böden geschätzt. Diese Datenbasis erlaubte es zudem zu untersuchen, inwieweit geostatistische Eigenschaften in Zusammenhang mit den Standortsbedingungen stehen. Für diesen Zweck entwickelten wir einen einfachen Ansatz zur Typisierung von Böden nach ihren räumlichen Eigenschaften. Beim BDF-Programm wurden auf jedem Standort 18 Proben in einem Kreuz-Transekt 18 x 18 m entnommen bei einem Mindestabstand von 3 m. Der Datenbestand umfasst die Parameter Corg und Ntot-Konzentrationen sowie -Vorräte und C / N - Verhältnis, effektive Kationen-Austausch-Kapazität (Ake), Basensättigung (BS), pH-Wert des Bodens und Grobbodenfraktion (> 2 mm). In die Metaanalyse wurden pro Bodenparameter 3780 Datensätze zur Erzeugung aggregierter Kennwerte einbezogen wie Schiefe, Variationskoeffizient (VK), Moran‘s I und den Anteil der räumlich strukturierten Varianz (SV) der Daten. Die beobachtete räumliche Struktur von Bodenparametern konnte zwar nicht eindeutig in Zusammenhang mit den Standortbedingungen gebracht werden. Es zeigt sich aber, dass die Gesamtvarianz einiger Parameter mit steigendem Tongehalt tendenziell zurück geht und dass das Niveau der Streuung (VK) der untersuchten Boden-Parameter sich erheblich unterscheidet. Die Rangfolge der Parameter hinsichtlich der Streuung ist pH-Wert (1), C/N - Verhältnis (2), C und N-Konzentration (3a), BS und Ake (3b), C- und N- Vorräte (4). Die Unsicherheitsabschätzung anhand der BDF Daten findet Eingang in das Fehlerbudget der zweiten bundesweiten Bodenzustandserhebung im Wald (BZE II) und dient dazu die Chancen zu bestimmen mit denen etwaige Bodenveränderungen nachgewiesen werden können.Data from the Bavarian forest soil monitoring programme (Bodendauerbeobachtungsfl ächen, BDF) were used to estimate the effect of smale scale variation on the uncertainty of soil characteristics and to evaluate a possible relation between spatial charactistics of soils and site conditions. We conducted a meta analysis of geostatistical parameters derived from 33 BDF sites. Within the BDF program 18 samples were taken at each site in an 18 x 18 m cross transect at minimum distance of 3 m. The data set involved Corg- and Ntot- concentrations and pools as well as the C/N-ratio, effective cation exchange capacity (CEC), base saturation (BS), pH and coarse soil fraction (> 2 mm). A total of 3780 records per soil parameter were used to calculate the skewness, the coeffi cient of variation (VK), Moran’s I and the portion of spatially structured variance in the data. Observed spatial patterns of soil paramters could not clearly be related to site conditions. However, total variance of some parameters tended to decrease with incresing clay content and the level of variation (VK) of the studied soil parameters differed signifi cantly. The ranking of parameters with respect to variation is (in ascending order): pH, C/N-ratio, C and N-concentration, BS und CEC, C- and N-pools. The results of this uncertainty estimation serve as an input to the error budget of the German forest soils survey (Level I monitoring; BZE II) and were used to estimate detectable soil changes within the framework of this program

Risk of elevated nitrate concentrations below forest in the region of Munich (South Bavaria) : regionalisation at the basis of remote sensing data and nested samples

Eine zielgerichtete Bewirtschaftung der Wälder im Hinblick auf die fortschreitende N-Sättigung fordert entsprechende Geoinformationen. Auf der Basis der Ergebnisse der Nitratinventur Bayern (Gensior et al 2003b, Mellert et al. 2005a) wurde eine Karte des Risikos erhöhter Nitratkonzentrationen für das Land Bayern erstellt (Mellert 2005c). Die Bayernkarte liefert Informationen über die durchschnittliche Situation in den forstlichen Wuchsgebieten und dient der Identifizierung von Problemregionen. Als Übersichtskarte kann sie jedoch die Bedürfnisse auf regionaler Ebene, z.B. für ein Wuchsgebiet, kaum befriedigen. Hierzu ist eine räumliche Präzisierung der Geodaten in einem detaillierten Maßstab erforderlich. Die bayernweite Regionalisierung basiert auf der in den Jahren 2001/2002 durchgeführten Nitratinventur im 8 x 8 km Raster (Level-I/BZE) an 399 Punkten im Flachland. Die Anzahl von Inventurpunkten in den einzelnen Wuchsgebieten ist daher sehr begrenzt. Zur Informationsverdichtung der kleinmaßstäbigen Bayernkarte auf den größeren Maßstab der Karte für den Großraum München wurden detaillierte Informationen aus einer 1998 durchgeführten Sickerwasserstudie (Rothe & Mellert 2004) herangezogen. Im vorliegenden Beitrag wird die Möglichkeit eines Downscalings durch ein genestets Verfahren vorgestellt. Die auf einem logistischem Regressionsmodell basierende Regionalisierung auf bayerischer Ebene (Meller et al. 2005c) wird hierbei mit den regionalen Daten durch ein multiples Regressionsverfahren verknüpft. Dank einer ins Projekt integrierten Pilotstudie zur Fernerkundung von Waldtypen konnte eine geeignete Waldkarte für den Raum München durch Klassifikation von Landsat-Daten bereit gestellt werden.A nested model design at two different spatial scales is presented to predict the risk of elevated nitrate concentrations below the main rooting zone of forests in the region of Munich. The procedure combines a logistic regression on the Bavarian scale with a general linear model (Mellert et al. 2005c) are used as a link between both scales. This link of information on two spatial levels and the identification of predictors at different scales are the advantages of this approach, compared to a sole and independent model. At the Bavarian scale the ammonium deposition and the precipitation as well as the stand type and the site conditions predict the risk of elevated nitrate concentration. At the regional scale the degree of forestation and the stand age contribute considerably to explain the variance of the nitrate concentrations. Among the sources of nitrogen input into the forests especially regional ammonium emissions by agriculture seem to contribute to the occurrence of high nitrate concentrations. The central outcome of this study is a map indicating the risk of elevated nitrate concentrations. The results suggests that forestry could contribute to retard nitrogen saturation and nitrate leaching by conversation of spruce into mixed stands with a high percentage of broad-leaved species

Orthorectification of helicopter-borne high resolution experimental burn observation from infra red handheld imagers

To pursue the development and validation of coupled fire-atmosphere models, the wildland fire modeling community needs validation data sets with scenarios where fire-induced winds influence fire front behavior, and with high temporal and spatial resolution. Helicopter-borne infrared thermal cameras have the potential to monitor landscape-scale wildland fires at a high resolution during experimental burns. To extract valuable information from those observations, three-step image processing is required: (a) Orthorectification to warp raw images on a fixed coordinate system grid, (b) segmentation to delineate the fire front location out of the orthorectified images, and (c) computation of fire behavior metrics such as the rate of spread from the time-evolving fire front location. This work is dedicated to the first orthorectification step, and presents a series of algorithms that are designed to process handheld helicopter-borne thermal images collected during savannah experimental burns. The novelty in the approach lies on its recursive design, which does not require the presence of fixed ground control points, hence relaxing the constraint on field of view coverage and helping the acquisition of high-frequency observations. For four burns ranging from four to eight hectares, long-wave and mid infra red images were collected at 1 and 3 Hz, respectively, and orthorectified at a high spatial resolution (<1 m) with an absolute accuracy estimated to be lower than 4 m. Subsequent computation of fire radiative power is discussed with comparison to concurrent space-borne measurementsPeer ReviewedPostprint (published version

Gas flaring activity and black carbon emissions in 2017 derived from the Sentinel-3A Sea and Land Surface Temperature Radiometer

Gas flares are a regionally and globally significant source of atmospheric pollutants. They can be detected by satellite remote sensing. We calculate the global flared gas volume and black carbon emissions in 2017 by applying (1) a previously developed hot spot detection and characterisation algorithm to all observations of the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board the Copernicus satellite Sentinel-3A and (2) newly developed filters for identifying gas flares and corrections for calculating both flared gas volumes (billion cubic metres, BCM) and black carbon (BC) emissions (g). The filter to discriminate gas flares from other hot spots uses the observed hot spot characteristics in terms of temperature and persistence. A regression function is used to correct for the variability of detection opportunities. A total of 6232 flaring sites are identified worldwide. The best estimates of the annual flared gas volume and the BC emissions are 129 BCM with a confidence interval of [35, 419 BCM] and 73 Gg with a confidence interval of [20, 239 Gg], respectively. Comparison of our activity (i.e. BCM) results with those of the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire data set and SWIR-based calculations show general agreement but distinct differences in several details. The calculation of black carbon emissions using our gas flaring data set with a newly developed dynamic assignment of emission factors lie in the range of recently published black carbon inventories, albeit towards the lower end. The data presented here can therefore be used e.g. in atmospheric dispersion simulations. The advantage of using our algorithm with Sentinel-3 data lies in the previously demonstrated ability to detect and quantify small flares, the long-term data availability from the Copernicus programme, and the increased detection opportunity of global gas flare monitoring when used in conjunction with the VIIRS instruments. The flaring activity and related black carbon emissions are available as “GFlaringS3” on the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) website (https://doi.org/10.25326/19, Caseiro and Kaiser, 2019)

Persistent Hot Spot Detection and Characterisation Using SLSTR

Gas flaring is a disposal process widely used in the oil extraction and processing industry. It consists in the burning of unwanted gas at the tip of a stack and due to its thermal characteristic and the thermal emission it is possible to observe and to quantify it from space. Spaceborne observations allows us to collect information across regions and hence to provide a base for estimation of emissions on global scale. We have successfully adapted the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire algorithm for the detection and characterisation of persistent hot spots, including gas flares, to the Sea and Land Surface Temperature Radiometer (SLSTR) observations on-board the Sentinel-3 satellites. A hot event at temperatures typical of a gas flare will produce a local maximum in the night-time readings of the shortwave and mid-infrared (SWIR and MIR) channels of SLSTR. The SWIR band centered at 1.61 &mu;m is closest to the expected spectral radiance maximum and serves as the primary detection band. The hot source is characterised in terms of temperature and area by fitting the sum of two Planck curves, one for the hot source and another for the background, to the radiances from all the available SWIR, MIR and thermal infra-red channels of SLSTR. The flaring radiative power is calculated from the gas flare temperature and area. Our algorithm differs from the original VIIRS Nightfire algorithm in three key aspects: (1) It uses a granule-based contextual thresholding to detect hot pixels, being independent of the number of hot sources present and their intensity. (2) It analyses entire clusters of hot source detections instead of individual pixels. This is arguably a more comprehensive use of the available information. (3) The co-registration errors between hot source clusters in the different spectral bands are calculated and corrected. This also contributes to the SLSTR instrument validation. Cross-comparisons of the new gas flare characterisation with temporally close observations by the higher resolution German FireBIRD TET-1 small satellite and with the Nightfire product based on VIIRS on-board the Suomi-NPP satellite show general agreement for an individual flaring site in Siberia and for several flaring regions around the world. Small systematic differences to VIIRS Nightfire are nevertheless apparent. Based on the hot spot characterisation, gas flares can be identified and flared gas volumes and pollutant emissions can be calculated with previously published methods