Laboratory for
Climatology and Remote Sensing

Abstract:

The increasing use of renewable energies as a source of electricity has led to a fundamental transition of the power supply system. The integration of ?uctuating weather-dependent energy sources into the grid already has amajor impact on its load ?ows. As a result, the interest in forecasting wind and solar radiation with a suf?cient accuracy over short time periods (<4 h) has grown. In this study, the short-term forecast of the effective cloud albedo based on optical ?ow estimation methods is investigated. The optical ?ow method utilized here is TV-L1 from the open source library OpenCV. This method uses a multi-scale approach to capture cloud motions on various spatial scales. After the clouds are displaced, the solar surface radiation will be calculated with SPECMAGIC NOW, which computes the global irradiation spectrally resolved from satellite imagery. Due to the high temporal and spatial resolution of satellite measurements, the effective cloud albedo and thus solar radiation can be forecasted from 5 min up to 4 h with a resolution of 0.05. The validation results of this method are very promising, and the RMSE of the 30-min, 60-min, 90-min and 120-min forecast equals 10.47%, 14.28%, 16.87% and 18.83%, respectively. The paper gives a brief description of the method for the short-term forecast of the effective cloud albedo. Subsequently, evaluation results will be presented and discussed.

Abstract:

Biological soil crusts (BSC) encompassing green algae, cyanobacteria, lichens, bryophytes, heterotrophic bacteria and microfungi are keystone species in arid environments because of their role in nitrogen- and carbon-fixation, weathering and soil stabilization, all depending on the photosynthesis of the BSC. Despite their importance, little is known about the BSCs of the Atacama Desert, although especially crustose chlorolichens account for a large proportion of biomass in the arid coastal zone, where photosynthesis is mainly limited due to low water availability. Here, we present the first hyperspectral reflectance data for the most wide-spread BSC species of the southern Atacama Desert. Combining laboratory and field measurements, we establish transfer functions that allow us to estimate net photosynthesis rates for the most common BSC species. We found that spectral differences among species are high, and differences between the background soil and the BSC at inactive stages are low. Additionally, we found that the water absorption feature at 1420 nm is a more robust indicator for photosynthetic activity than the chlorophyll absorption bands. Therefore, we conclude that common vegetation indices must be taken with care to analyze the photosynthesis of BSC with multispectral data.

Abstract:

Temperate grasslands play globally an important role, for example, for biodiversity conservation, livestock forage production, and carbon storage. The latter two are primarily controlled by biomass production, which is assumed to decrease with lower amounts and higher variability of precipitation, while increasing air temperature might either foster or suppress biomass production. Additionally, a higher atmospheric CO2 concentration ([CO2]) is supposed to increase biomass productivity either by directly stimulating photosynthesis or indirectly by inducing water savings (CO2 fertilization effect). Consequently, future biomass productivity is controlled by the partially contrasting effects of changing climatic conditions and [CO2], which to date are only marginally understood. This results in high uncertainties of future biomass production and carbon storage estimates. Consequently, this study aims at statistically estimating mid-21st century grassland aboveground biomass (AGB) based on 18 years of data (1998–2015) from a free air carbon enrichment experiment. We found that lower precipitation totals and a higher precipitation variability reduced AGB. Under drier conditions accompanied by increasing air temperature, AGB further decreased. Here AGB under elevated [CO2] was partly even lower compared to AGB under ambient [CO2], probably because elevated [CO2] reduced evaporative cooling of plants, increasing heat stress. This indicates a higher susceptibility of AGB to increased air temperature under future atmospheric [CO2]. Since climate models for Central Europe project increasing air temperature and decreasing total summer precipitation associated with an increasing variability, our results suggest that grassland summer AGB will be reduced in the future, contradicting the widely expected positive yield anomalies from increasing [CO2].

Abstract:

Fog has a substantial influence on various ecosystems and it impacts economy, traffic systems and human life in many ways. In order to be able to deal with the large number of influence factors, a spatially explicit high-resoluted data set of fog frequency distribution is needed. In this study, a hybrid approach for fog retrieval based on Meteosat Second Generation (MSG) data and ground truth data is presented. The method is based on a random forest (RF) machine learning model that is trained with cloud base altitude (CBA) observations from Meteorological Aviation Routine Weather Reports (METAR) as well as synoptic weather observations (SYNOP). Fog is assumed where the model predicts CBA values below a dynamically derived threshold above the terrain elevation. Cross validation results show good accordance with observation data with a mean absolute error of 298 m in CBA values and an average Heidke Skill Score of 0.58 for fog occurrence. Using this technique, a 10 year fog baseline climatology with a temporal resolution of 15 min was derived for Europe for the period from 2006 to 2015. Spatial and temporal variations in fog frequency are analyzed. Highest average fog occurrences are observed in mountainous regions with maxima in spring and summer. Plains and lowlands show less overall fog occurrence but strong positive anomalies in autumn and winter.

Abstract:

The Atacama Desert is well known for the high occurrence of large-scale fog (spatial extents: hundreds of kilometers) emerging as low stratus (LST) decks over the Pacific Ocean. By contrast, the small-scale and heterogeneous occurrence of small-scale fog (hundreds of meters) particularly during summers is widely unconsidered. However, these events are important for the local vegetation and particularly for the biological soil crusts (BSC) that are widely distributed in this extreme ecosystem. Consequently, a case study in a typical fog oasis in the Pan de Azúcar National Park was conducted to test the feasibility combining field measurements, drone profiling, remote sensing and numerical modeling (i) to investigate fog-type specific differences regarding dynamics, physical properties and formation, (ii) to test the applicability of remote sensing technology for fog monitoring based on existing low-resolution and a proposed new high-resolution product and (iii) to estimate the related fog water input to BSCs. Two types of fog were observed. The well-known fog/LST deck emerging from the Pacific Ocean with high water path and large spatial extent was the first type. Fog of the second type was patchier, small-scale and not necessarily connected to the LST over the ocean. Instead, fog formation of the second type was related to thermal breeze systems, which produced shallow clouds containing less water than those of type 1. In general, such small-scale fog events were not captured well by existing remote sensing products but could be detected with the proposed new high-resolution product which provided promising results. Both fog types were important water resources for the BSCs, with approximately 8% to 24% of the fog water flux available to the BSCs at the surface. The results indicated the feasibility of the proposed methods’ pool to estimate the water budget of BSCs with a high spatial resolution in the future.

Abstract:

Environmental stressors and changes in land use have led to rapid and dramatic species losses. As such, we need effective monitoring programs that alert us not only to biodiversity losses, but also to functional changes in species assemblages and associated ecosystem processes. Ants are important components of terrestrial food webs and a key group in food web interactions and numerous ecosystem processes. Their sensitive and rapid response to environmental changes suggests that they are a suitable indicator group for the monitoring of abiotic, biotic, and functional changes. We tested the suitability of the incidence (i.e. the sum of all species occurrences at 30 baits), species richness, and functional richness of ants as indicators of ecological responses to environmental change, forest degradation, and of the ecosystem process predation on herbivorous arthropods. We sampled data along an elevational gradient (1000–3000 m a.s.l.) and across seasons (wetter and drier period) in a montane rainforest in south- ern Ecuador. The incidence of ants declined with increasing elevation but did not change with forest degradation. Ant incidence was higher during the drier season. Species richness was highly correlated with incidence and showed comparable results. Functional richness also declined with increasing ele- vation and did not change with forest degradation. However, a null-model comparison revealed that the functional richness pattern did not differ from a pattern expected for ant assemblages with ran- domly distributed sets of traits across species. Predation on arti?cial caterpillars decreased along the elevational gradient; the pattern was not driven by elevation itself, but by ant incidence (or inter- changeable by ant richness), which positively affected predation. In spite of lower ant incidence (or ant richness), predation was higher during the wetter season and did not change with forest degrada- tion and ant functional richness. We used path analysis to disentangle the causal relationships of the environmental factors temperature (with elevation as a proxy), season, and habitat degradation with the incidence and functional richness of ants, and their consequences for predation. Our results would suggest that the forecasted global warming might support more active and species-rich ant assem- blages, which in turn would mediate increased predation on herbivorous arthropods. However, this prediction should be made with reservation, as it assumes that the dispersal of ants keeps pace with the climatic changes as well as a one-dimensional relationship between ants and predation within a food-web that comprises species interactions of much higher complexity. Our results also suggested that degraded forests in our study area might provide suitable habitat for epigaeic, ground-dwelling ant assemblages that do not differ in incidence, species richness, functional richness, composition, or predation on arthropods from assemblages of primary forests. Most importantly, our results suggest that the occurrence and activity of ants are important drivers of ecosystem processes and that changes in the incidence and richness of ants can be used as effective indicators of responses to temperature changes and of predation within mega-diverse forest ecosystems

Abstract:

Land-use and climate change are major threats to biodiversity and ecosystem functions. Most of the current biodiversity monitoring systems are based on periodic records of the populations of a set of threatened or popular ‘?agship’ indicator species. In contrast to the abundance-based monitoring of species, also speci?c indicators of processes and functional interactions in an ecosystem may become targets of a more functional monitoring which can unveil early responses of an ecosystem to environmental changes at different spatial and temporal scales. The contributions of this Special Issue present such functional indicators for assessing and predicting responses to environmental changes of ecosystem functions in a hotspot of tropical biodiversity.

Abstract:

The effects of an increasing moisture on trees of the tropical species-rich mountain rain forest in the South Ecuadorian Andes was investigated, using the daily total water consumption (TWC) and the instantaneous water use ef?ciency (WUE, ratio of photosynthetic CO2 uptake per water loss by transpiration) as eco- physiological indicators. Two canopy and one sub-canopy tree species, (Vismia tomentosa, Clusiaceae, an as of yet unknown Lauracee, and Spirotheca rosea, Bombacaceae) were the experimental objects. Seasonal changes as well as a long-term (18 months) trend of increasing precipitation caused an inverse reaction of the TWC of the trees. Because of a rather unlimited water supply to the trees from a permanently high water content of the soil, transpiration followed mainly the atmospheric demand of water vapor, and increasing moisture hence reduced water loss by transpiration. It was hypothesized that in spite of the reduction in transpiratory water loss photosynthetic carbon acquisition would be not or less affected due to an increase in water use ef?ciency. Concomitant measurements of photosynthetic net CO2 uptake showed the expected increase of WUE in V. tomentosa and S. rosea, but no clear reaction of the Lauracee. Accompanying measurements of stem extension growth con?rmed an undiminished growth of V. tomen- tosa and S. rosea but showed also suspended growth of the Lauracee during the wettest months. While TWC can be continuously monitored with the heat dissipation technique, WUE is determined by leaf porometry in campaigns for which access to the canopy is required. Simultaneous recordings of the gas exchange of leaves at 4 different positions in the crown of one of the experimental trees (V. tomentosa) showed the usability of the trait WUE in combination with the total daily water consumption as indicator set for assessing the response of trees to a subtly changing climate. However, not all tree species appear as likewise useful indicator trees

Abstract:

Grünländer der gemäßigten Breiten liefern wichtige Ökosystemdienstleistungen die von der Biomasseproduktion abhängen. Letztere nimmt mit geringeren Niederschlägen und höheren Niederschlagsvariabilitäten ab, während höhere Lufttemperaturen fördernd wirken können. Zusätzlich sollen höhere atmosphärische CO2-Konzentrationen ([CO2]s) die Biomasse durch direkte Stimulation der Photosynthese und Wassereinsparung (CO2-Düngeeffekt) erhöhen. Somit ist die Biomasseproduktivität durch teils gegenläufige Auswirkungen wechselnder klimatischer Bedingungen und [CO2] gesteuert. Die Studie untersucht diese Einflüsse des globalen Wandels auf Grundlage eines Freiluftexperiments zur CO2-Anreicherung (~20% über Umgebungs-[CO2]; 18 J.) um die oberirdische Sommer-Biomasse (AGB) Mitte des 21. Jhdt. statistisch vorherzusagen. Ein informationstheoretisches Screening lieferte die wichtigsten Prädiktoren, basierend auf Lufttemperatur- und Niederschlagsmessungen. Die AGB-Produktion wurde für verschiedene Klimaregime, abgeleitet aus den Beobachtungen des Versuchszeitraums, geschätzt. Wir fanden, dass die zukünftige AGB-Produktion hauptsächlich von der Niederschlagsmenge abhängt, gefolgt von Lufttemperatur und Niederschlagsvariabilität. Variablere Niederschläge reduzierten die AGB und umgekehrt. Die AGB-Produktion unter trockenen Bedingungen verringerte sich mit steigenden Lufttemperaturen weiter. Im Kontrast zu weithin erwarteter Ertragssteigerungen durch erhöhte [CO2]s, führen solche Bedingungen zu AGB-Vorhersagen unter denen der aktuellen AGBs. Da Klimamodelle für Sommer in Mitteleuropa steigende Lufttemperaturen und abnehmende Niederschlagsmengen mit zunehmender Variabilität projizieren, deuten unsere Ergebnisse trotz steigender [CO2]s auf eine reduzierte zukünftige Grünland-Sommer-AGB hin.

Abstract:

This study investigates the temporal dynamics of the drop size distribution (DSD) and its influence on the relationship between the liquid water content (LWC) and the radar reflectivity (Z) in fogs. Data measured during three radiation fog events at the Marburg Ground Truth and Profiling Station in Linden-Leihgestern, Germany, form the basis of this analysis. Specifically, we investigated the following questions: (1) Do the different fog life cycle stages exhibit significantly different DSDs? (2) Is it possible to identify characteristic DSDs for each life cycle stage? (3) Is it possible to derive reliable Z-LWCrelationships by means of a characteristic DSD? The results showed that there were stage-dependent differences in the fog life cycles, although each fog event was marked by unique characteristics, and a general conclusion about the DSD during the different stages could not be made. A large degree of variation within each stage also precludes the establishment of a representative average spectrum.

Abstract:

This paper focuses on the analysis of precipita- tion patterns, using a Local Area Weather Radar to collect information about the precipitation distribution in an Andean region of southern Ecuador (cities of Loja, Zamora and Catamayo). 54 representative events were selected to develop daily precipitation maps and to obtain their rele- vant characteristics, which were related to the topography and the season. The results showed that a strong correlation between the areas covered by precipitation (RA coef?cient) and the season exists. In general, humid air masses come from the east (Amazon Basin), but during the main rainy season (December to April), humidity also frequently enters the study region from the west (Paci?c Ocean). The rainy season is characterized by convective precipitation, associated with higher evaporation rates during austral summer. The relatively dry season is formed between May and November, but considerable precipitation amounts are registered, too, due to advective moisture transport from the Amazon Basin, a result of the predominant tropical easterlies carrying the humidity up the eastern escarpment of the Andes, generally following the natural course of the drainage systems.

Abstract:

Texture information from passive remote sensing images provides surrogates for habitat structure, which is relevant for modeling biodiversity across space and time and for developing effective ecological indicators. However, the applicability of this information might differ among taxa and diversity measures. We compared the ability of indicators developed from texture analysis of remotely sensed images to predict species richness and species turnover of six taxa (trees, pyraloid moths, geometrid moths, arctiinae moths, ants, and birds) in a megadiverse Andean mountain rainforest ecosystem. Partial least-squares regression models were fitted using 12 predictors that characterize the habitat and included three topographical metrics derived from a high-resolution digital elevation model and nine texture metrics derived from very high-resolution multi-spectral orthophotos. We calculated image textures derived from mean, correlation, and entropy statistics within a relatively broad moving window (102 m × 102 m) of the near infra-red band and two vegetation indices. The model performances of species richness were taxon dependent, with the lowest predictive power for arctiinae moths (4%) and the highest for ants (78%). Topographical metrics sufficiently modeled species richness of pyraloid moths and ants, while models for species richness of trees, geometrid moths, and birds benefited from texture metrics. When more complexity was added to the model such as additional texture statistics calculated from a smaller moving window (18 m × 18 m), the predictive power for trees and birds increased significantly from 12% to 22% and 13% to 27%, respectively. Gradients of species turnover, assessed by non-metric two-dimensional scaling (NMDS) of Bray-Curtis dissimilarities, allowed the construction of models with far higher predictability than species richness across all taxonomic groups, with predictability for the first response variable of species turnover ranging from 64% (birds) to 98% (trees) of the explained change in species composition, and predictability for the second response variable of species turnover ranging from 33% (trees) to 74% (pyraloid moths). The two NMDS axes effectively separated compositional change along the elevational gradient, explained by a combination of elevation and texture metrics, from more subtle, local changes in habitat structure surrogated by varying combinations of texture metrics. The application of indicators arising from texture analysis of remote sensing images differed among taxa and diversity measures. However, these habitat indicators improved predictions of species diversity measures of most taxa, and therefore, we highly recommend their use in biodiversity research.

Abstract:

Though the relevance of pasture degradation on the Qinghai-Tibet Plateau (QTP) is widely postulated, its extent is still unknown. Due to the enormous spatial extent, remote sensing provides the only possibility to investigate pasture degradation via frequently used proxies such as vegetation cover and aboveground biomass (AGB). However, unified remote sensing approaches are still lacking. This study tests the appli- cability of hyper- and multispectral in situ measurements to map vegetation cover and AGB on regional scales. Using machine learning techniques, it is tested whether the full hyperspectral information is needed or if multispectral information is sufficient to accurately estimate pasture degradation prox- ies. To regionalize pasture degradation proxies, the transferability of the locally derived ML-models to high resolution multispectral satellite data is assessed. 1183 hyperspectral measurements and vegeta- tion records were performed at 18 locations on the QTP. Random Forests models with recursive feature selection were trained to estimate vegetation cover and AGB using narrow-band indices (NBI) as predic- tors. Separate models were calculated using NBI from hyperspectral data as well as from the same data resampled to WorldView-2, QuickBird and RapidEye channels. The hyperspectral results were compared to the multispectral results. Finally, the models were applied to satellite data to map vegetation cover and AGB on a regional scale. Vegetation cover was accurately predicted by Random Forest if hyperspectral measurements were used (cross validated R2 = 0.89). In contrast, errors in AGB estimations were consid- erably higher (cross validated R2 = 0.32). Only small differences in accuracy were observed between the models based on hyperspectral compared to multispectral data. The application of the models to satellite images generally resulted in an increase of the estimation error. Though this reflects the challenge of applying in situ measurements to satellite data, the results still show a high potential to map pasture degradation proxies on the QTP. Thus, this study presents robust methodology to remotely detect and monitor pasture degradation at high spatial resolutions.