Laboratory for
Climatology and Remote Sensing

Abstract:

Atmospheric sulfur deposition above certain limits can represent a threat to tropical forests, causing nutrient imbalances and mobilizing toxic elements that impact biodiversity and forest productivity. Atmospheric sources of sulfur deposited by precipitation have been roughly identified in only a few lowland tropical forests. Even scarcer are studies of this type in tropical mountain forests, many of them mega- diversity hotspots and especially vulnerable to acidic deposition. In these places, the topographic complexity and related streamflow conditions affect the origin, type, and intensity of deposition. Furthermore, in regions with a variety of natural and anthropogenic sulfur sources, like active volcanoes and biomass burning, no source emission data has been used for determining the contribution of each source to the deposition. The main goal of the current study is to evaluate sulfate (SO?4) deposition by rain and occult precipitation at two to- pographic locations in a tropical mountain forest of southern Ecuador, and to trace back the deposition to possible emission sources applying back-trajectory modeling. To link upwind natural (volcanic) and anthropogenic (urban/industrial and biomass-burning) sulfur emissions and observed sulfate deposition, we employed state-of-the-art inventory and satellite data, including volcanic passive degassing as well. We conclude that biomass-burning sources generally dominate sulfate deposition at the evaluated sites. Minor sulfate transport occurs during the shifting of the predominant winds to the north and west. Occult precipitation sulfate deposition and likely rain sulfate deposition are mainly linked to biomass-burning emissions from the Amazon lowlands. Volcanic and anthropogenic emissions from the north and west contribute to occult precipitation sulfate deposition at the mountain crest Cerro del Consuelo meteorological station and to rain-deposited sulfate at the upriver mountain pass El Tiro meteorological station.

Abstract:

A new method is presented for the determination of cloud top heights using the footage of a time-lapse camera that is placed above a frequently occurring cloud layer in a mountain valley. Contact points between cloud tops and underlying terrain are automatically detected in the camera image based on differences in the brightness, texture and movement of cloudy and non-cloudy areas. The height of the detected cloud top positions is determined by comparison with a digital elevation model projected to the view of the camera. The technique has been validated using data about the cloud immersion of a second camera as well as via visual assessment. The validation shows a high detection quality, especially regarding the requirements for the validation of satellite cloud top retrieval algorithms.

Abstract:

This paper describes a method of low-altitude remote sensing in combination with in situ measurements (leaf area, spectroscopy, and position) to monitor the postfire canopy recovery of two competing grassland species. The method was developed in the Andes of Ecuador, where a tethered balloon with a digital camera was deployed to record a time series of very high spatial resolution imagery (nominal resolution=2 cm ) of an experimental plot covered by two competing species: 1) the pasture grass, Setaria sphacelata; and 2) the invasive southern bracken, Pteridium arachnoideum. Image processing techniques were combined to solve geometric issues and construct high-quality mosaics for image classification. The semiautomatic and object-oriented classification method was based on geometrical and textural attributes of image segments and showed promising results for detecting the invasive bracken fern in Setaria pastures (performance by area under the curve, AUC = 0.88). Valuable insights are given into vegetation monitoring applications using unmanned aerial vehicles, which produces a time series of species-specific maps, including foliage projective cover (FPC) and leaf area index (LAI). This new method constitutes an important and accessible tool for ecological investigations of competing species in pastures and validation of remote sensing information on mountain environments.

Abstract:

The present study aims to investigate the potential of the random forests ensemble classification and regression technique to improve rainfall rate assignment during day, night and twilight (resulting in 24-hour precipitation estimates) based on cloud physical properties retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data. Random forests (RF) models contain a combination of characteristics that make them well suited for its application in precipitation remote sensing. One of the key advantages is the ability to capture non-linear association of patterns between predictors and response which becomes important when dealing with complex non-linear events like precipitation. Due to the deficiencies of existing optical rainfall retrievals, the focus of this study is on assigning rainfall rates to precipitating cloud areas in connection with extra-tropical cyclones in mid-latitudes including both convective and advective-stratiform precipitating cloud areas. Hence, the rainfall rates are assigned to rain areas previously identified and classified according to the precipitation formation processes. As predictor variables water vapor-IR differences and IR cloud top temperature are used to incorporate information on cloud top height. ?T8.7–10.8 and ?T10.8–12.1 are considered to supply information about the cloud phase. Furthermore, spectral SEVIRI channels (VIS0.6, VIS0.8, NIR1.6) and cloud properties (cloud effective radius, cloud optical thickness) are used to include information about the cloud water path during daytime, while suitable combinations of temperature differences (?T3.9–10.8, ?T3.9–7.3) are considered during night-time. The development of the rainfall rate retrieval technique is realised in three steps. First, an extensive tuning study is carried out to customise each of the RF models. The daytime, night-time and twilight precipitation events have to be treated separately due to differing information content about the cloud properties between the different times of day. Secondly, the RF models are trained using the optimum values for the number of trees and number of randomly chosen predictor variables found in the tuning study. Finally, the final RF models are used to predict rainfall rates using an independent validation data set and the results are validated against co-located rainfall rates observed by a ground radar network. To train and validate the model, the radar-based RADOLAN RW product from the German Weather Service (DWD) is used which provides area-wide gauge-adjusted hourly precipitation information. Regarding the overall performance, as indicated by the coefficient of determination (Rsq), hourly rainfall rates show already a good correlation with Rsq = 0.5 (day and night) and Rsq = 0.48 (twilight) between the satellite and radar based observations. Higher temporal aggregation leads to better agreement. Rsq rises to 0.78 (day), 0.77 (night) and 0.75 (twilight) for 8-h interval. By comparing day, night and twilight performance it becomes evident that daytime precipitation is generally predicted best by the model. Twilight and night-time predictions are generally less accurate but only by a small margin. This may due to the smaller number of predictor variables during twilight and night-time conditions as well as less favourable radiative transfer conditions to obtain the cloud parameters during these periods. However, the results show that with the newly developed method it is possible to assign rainfall rates with good accuracy even on an hourly basis. Furthermore, the rainfall rates can be assigned during day, night and twilight conditions which enables the estimation of rainfall rates 24 h day.

Abstract:

The Tibetan Plateau suffers from progressive degradation caused by over-grazing due to improper live- stock management, global climate change and herbivory from small mammals. Therefore, a robust indicator system for rangeland degradation has to be developed and tested. This paper investigates local patterns of degradation at two sites (Lake Namco and Mt. Kailash) in Xizang province (China) that are cov- ered by vegetation types typical of a large portion of the plateau. The suitability of a two-indicator system is analysed using hyperspectral field measurements, and its transferability to spaceborne data is tested. The indicators are (1) land-cover fractions derived from linear spectral unmixing and (2) chlorophyll content as a proxy for nutrient and water availability calculated using hyperspectral vegetation indices and partial least squares regression. Because cattle remain near settlements overnight in the local semi- nomadic pastoral system, it can be expected that grazing intensity is highest near the settlement and declines with increasing distance. Therefore, we tested the effect of distance on both indicators using a Spearman correlation analysis. The predicted chlorophyll content and land cover fractions of the indica- tor system were in good agreement with field observations (correlation coefficients between 0.70 and 0.98). High correlations between distance from settlements and land-cover fractions at both study sites demonstrated that the land-cover fraction is a reliable indicator for degradation. A positive correlation between distance from settlements and photosynthetically active vegetation (PV) revealed over-grazing patterns at the first site. Furthermore, the chlorophyll indicator was proven suitable because chlorophyll concentration declined with increasing distance from settlements. This underlines the over-grazing pat- tern because cattle excrement was the only external source of nutrients in the ecosystem and it was positively correlated with grazing intensity. However, at the second site, we found a significant positive effect of distance on the amount of photosynthetically non-active vegetation; no effect of distance on PV and chlorophyll content was found. Therefore, no evidence of pasture degradation was detected at the second site. Regarding the potential use of satellite data for degradation monitoring, we found that (1) the land-cover indicator derived from multispectral data was more robust than using noise-filtered hyper- spectral information and (2) the chlorophyll amount indicator was estimated from simulated EnMAP data with low error rates. Because the proposed two-indicator system can be derived from multi- and hyperspectral satellite data and combines site conditions and local plant cover, it provides a time-saving and robust method to measure pasture degradation across large areas, assuming that respective satellite data are available.

Abstract:

Heavy natural disturbance in large protected areas of former commercial forests increasingly evokes European parliaments to call for management intervention because a loss of habitats and species is feared. In contrast, natural early successional habitats have recently been recognised as important for conservation. Current knowledge in this field mostly results from studies dealing only with selected taxa. Here we analyse the success of species across 24 lineages of three kingdoms in the Bavarian Forest National Park (Germany) after 15 years of a European spruce bark beetle (Ips typographus L.) outbreak that led to rapid canopy opening. Using indicator species analysis, we found 257 species with a significant preference for open forests and 149 species with a preference for closed forests, but only 82 species with a preference for the stand conditions transitional between open and closed forests. The large number of species with a preference for open forests across lineages supports the role of this bark beetle as a keystone species for a broad array of species. The slowdown of the outbreak after 15 years in the core zone of the national park resulted in less than half of the area being affected, due to variability in stand ages and tree species mixtures. Our case study is representative of the tree species composition and size of many large protected montane areas in Central European countries and illustrates that (1) natural disturbances increase biodiversity in formerly managed forests and (2) a montane protected area spanning 10,000 ha of low range mountains is likely sufficient to allow natural disturbances without a biased loss of closed-forest species.

Abstract:

Future increase of atmospheric nitrogen deposition in tropical regions is expected to have negative impacts on forests ecosystems and related biogeochemical processes. In tropical mountain forests topography causes complex streamflow and rainfall patterns, governing the atmospheric transport of pollutants and the intensity and spatial variability of deposition. The main goal of the current study is to link spatio-temporal patterns of upwind nitrogen emissions and nitrate deposition in the San Francisco Valley (eastern Andes of southern Ecuador) at different altitudinal levels. The work is based on Scanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) retrieved-NO2 concentrations, NOx biomass burning emissions from the Global Fire Emissions Database (GFEDv3), and regional vehicle emissions inventory (SA-INV) for urban emissions in South America. The emission data is used as input for lagrangian atmospheric backward trajectory modeling (FLEXTRA) to model the transport to the study area. The results show that NO3?NO3? concentrations in occult precipitation samples are significantly correlated to long-range atmospheric secondary nitrogen transport at the highest meteorological stations (MSs) only, whereas for NO3?NO3? concentrations in rain samples this correlation is more pronounced at the lower MSs. We conclude that ion concentrations in occult precipitation at the uppermost MSs are mainly linked to distant emission sources via the synoptic circulation impinging the more exposed higher sites. Lower correlations close to the valley bottom are due to a lower occult precipitation frequency and point to a contamination of the samples by local pollution sources not captured by the used emission data sources.

Abstract:

[1] Previous studies of clouds over the Tibetan Plateau (TiP) were subject to limitations. Surface observations are scarce, and satellite retrievals are not well adapted to the peculiar conditions of the TiP. For the most comprehensive existing cloud data set, provided by the International Satellite Cloud Climatology Project (ISCCP), issues were reported for the TiP. It also lacks sufficient spatiotemporal resolution for this topographically complex region. With the Indian Ocean Data Coverage service, European Organisation for the Exploitation of Meteorological Satellites provides a Meteosat data set between 1998 and 2008. The resolution of around 6?km at the study area is sufficient even for complex terrain. Based on this data set and on products of the active sensor onboard CloudSat, we develop a novel gross-cloud retrieval for the TiP using logistic regression models. The approach maintains the original Meteosat resolution. Validation against independent CloudSat data reveals good performance. The approach also outperforms the ISCCP pixel level (DX) data set. The resulting data set is the first for the TiP that provides cloud information with sufficient resolution for both day and night. Patterns of cloud frequencies during winter, premonsoon, and monsoon seasons are analyzed. Strong diurnal forcing is found for the plateau. Peaks of cloud frequencies above the slopes occur during afternoon, while they are delayed in the valleys, where high cloud frequencies persist throughout the nights. Above the lower parts of the southern foothills of the Himalayas cloud frequencies were for the first time found to increase until the early morning. Katabatic flows are suspected to be responsible for this pattern by initiating the formation of mesoscale convective systems.

Abstract:

In the anthropogenic fire-disturbed ecosystem of the San Francisco Valley in the Andes of southeastern Ecuador, dense stands of an aggressive invasive weed, the southern bracken fern (Pteridium arachnoideum and Pteridium caudatum), dominate the landscape. To secure sustainable land management in the region, a comprehensive understanding of bracken spatial-distribution patterns and life cycle dynamics is crucial. We investigated the possibility of detecting bracken-infested areas and frond status (live, fungi-infected, and dead) by means of a high-resolution QuickBird scene from October 2010 and spectral signatures based on field spectroscopy. After image pre-processing, a two-step classification procedure first delineates the bracken-infested area by means of a maximum-likelihood hard classification. The probability-guided unmixing classifier with field-derived end-members is applied in the second step to obtain the fractional cover of the different frond statuses per pixel. The results showed that the areas infested by bracken could be distinguished from the other land-cover classes with high accuracy (overall accuracy of 0.9973). Also, the three frond statuses could be accurately classified at the sub-pixel level. The ‘dead’ class was the dominant frond status at the time of image acquisition (October 2010). We conclude that the extreme dry spell in October 2010 was particularly responsible for this dominance.

Abstract:

An objective classification of radiation fog in distinct evolutionary stages during its life cycle based on reliable criteria is essential for various applications, for example for numerical fog modelling and fog forecasting. However, there have been  up to now  merely qualitative approaches for the distinction of different evolutionary stages in radiation fog. Measurements of the microphysical fog properties with an optical particle counter obtained from droplet measurement technologies together with recordings of the horizontal visibility (VIS) are statistically analyzed to determine individual evolutionary stages of radiation fog with consistent microphysical properties. The developed three-stage approach is based on a statistical change point analysis of the double sum curves of the VIS, the liquid water content, the droplet concentration and the mean radius of the drop size distributions. It could be shown that each of the three recorded radiation fog occurrences could be split into three consecutive phases from formation to dissipation, regardless whether the VIS or the microphysical properties were considered. Having featured consistent microphysical patterns, it could be assumed that the three separated phases of the single fog occurrence could be aggregated for radiation fog. Although this classification is statistically reliable, the dataset still has to be extended for a generalization concerning the separated evolutionary stages.

Abstract:

Knowledge of cloud properties such as cloud effective radius (aef) and optical thickness (?) is essential to understand their role in the dynamic radiation budget and climate change. The Spinning Enhanced Visible and Infrared Instrument (SEVIRI) on board Meteosat Second Generation (MSG) with its high temporal resolution (15 min), permits a quasi-continuous monitoring of the evolution of cloud properties. This has motivated the adaptation of the SLALOM (SimpLe Approximations for cLOudy Media) algorithm, a semi-analytical cloud property retrieval technique to MSG SEVIRI. The optical properties retrieved by SLALOM are compared against the well known and validated NASA MODIS cloud property product (MODIS 06) as well as the cloud optical depth product (2B-TAU) of CloudSat. The results are shown over the North Atlantic and over the European continent with the intention of determine the relative accuracy between SLALOM and the other retrievals. Over the North Atlantic, SLALOM-based cloud properties retrieved from SEVIRI datasets show a good agreement with the MODIS 06 product with correlation coefficients of 0.93 (?) and 0.82 (aef). The largest deviations were found in less homogeneous cloud areas that are characterized by broken clouds and toward the cloud borders. Moreover, SLALOM optical thickness values are well within the range of corresponding CloudSat 2B-TAU optical thickness values which can be found within a SEVIRI pixel, except for ? < 5 where SLALOM tends to overestimate ?. Despite the different sensor characteristics and viewing geometries, the retrieved cloud properties compare very well. Over Europe, the evaluation between SLALOM and MODIS 06 showed larger differences. We attribute this to (a) uncertainties related to the surface albedo which is treated differently in the algorithms and is based on different albedo maps and (b) inhomogeneities of clouds which exhibit quite complex structures particularly over land. The latter are detected on different scales by MODIS and SEVIRI because of their different spatial resolutions. Given the demonstrated accuracy of SLALOM using MSG SEVIRI data there is a wide spread of potential applications.