A possible role of ground-based microorganisms on cloud formation in the atmosphere

The formation of clouds is an important process for the atmosphere, the hydrological cycle, and climate, but some aspects of it are not completely understood. In this work, we show that microorganisms might affect cloud formation without leaving the Earth's surface by releasing biological surfactants (or biosurfactants) in the environment, that make their way into atmospheric aerosols and could significantly enhance their activation into cloud droplets. In the first part of this work, the cloud-nucleating efficiency of standard biosurfactants was characterized and found to be better than that of any aerosol material studied so far, including inorganic salts. These results identify molecular structures that give organic compounds exceptional cloud-nucleating properties. In the second part, atmospheric aerosols were sampled at different locations: a temperate coastal site, a marine site, a temperate forest, and a tropical forest. Their surface tension was measured and found to be below 30 mN/m, the lowest reported for aerosols, to our knowledge. This very low surface tension was attributed to the presence of biosurfactants, the only natural substances able to reach to such low values. The presence of strong microbial surfactants in aerosols would be consistent with the organic fractions of exceptional cloud-nucleating efficiency recently found in aerosols, and with the correlations between algae bloom and cloud cover reported in the Southern Ocean. The results of this work also suggest that biosurfactants might be common in aerosols and thus of global relevance. If this is confirmed, a new role for microorganisms on the atmosphere and climate could be identified.

Land use/ land cover change and its impact on soil erosion process in Begnas Tal Rupa Tal watershed using geospatial tools, Kaski district, Nepal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Detecting and evaluating land cover change in the eastern half of East Timor (1972-2011)

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Assessing the impacts of land use and land cover change on hydrology of watershed: a case study on Gigel-Abbay Watershed, Lake Tana Basin, Ethiopia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Structure and composition of the ground-herb community in a terra-firme Central Amazonian forest

The herb community of tropical forests is very little known, with few studies addressing its structure quantitatively. Even with this scarce body of information, it is clear that the ground herbs are a rich group, comprising 14 to 40% of the species found in total species counts in tropical forests. The present study had the objective of increasing the knowledge about the structure and composition of the ground-herb community and to compare the sites for which there are similar studies. The study was conducted in a tropical non-inundated and evergreen forest 90 km north of Manaus, AM. Ground herbs were surveyed in 22 transects of 40 m², distributed in five plots of 4 ha. The inventoried community was composed of 35 species, distributed in 24 genera and 18 families. Angiosperms were represented by 8 families and Pteridophytes by 10 families. Marantaceae (12 sp) and Cyperaceae (4 sp) were the richest families. Marantaceae and Poaceae were the families with greatest abundance and cover. Marantaceae, Poaceae, Heliconiaceae and Pteridophytes summed 96% of total herb cover, and therefore were responsible for almost all the cover of the community. The 10 most important species had 83.7% of the individuals. In general, the most abundant species were also the most frequent. Richness per transect varied from 7 to 19 species, and abundance varied from 30 to 114 individuals. The community structure was quite similar to 3 other sites in South America and one site in Asia.

A comparative evaluation of endemic and non-endemic region of visceral leishmaniasis (Kala-azar) in India with ground survey and space technology

In visceral leishmaniasis, phlebotomine vectors are targets for control measures. Understanding the ecosystem of the vectors is a prerequisite for creating these control measures. This study endeavours to delineate the suitable locations of Phlebotomus argentipes with relation to environmental characteristics between endemic and non-endemic districts in India. A cross-sectional survey was conducted on 25 villages in each district. Environmental data were obtained through remote sensing images and vector density was measured using a CDC light trap. Simple linear regression analysis was used to measure the association between climatic parameters and vector density. Using factor analysis, the relationship between land cover classes and P. argentipes density among the villages in both districts was investigated. The results of the regression analysis indicated that indoor temperature and relative humidity are the best predictors for P. argentipes distribution. Factor analysis confirmed breeding preferences for P. argentipes by landscape element. Minimum Normalised Difference Vegetation Index, marshy land and orchard/settlement produced high loading in an endemic region, whereas water bodies and dense forest were preferred in non-endemic sites. Soil properties between the two districts were studied and indicated that soil pH and moisture content is higher in endemic sites compared to non-endemic sites. The present study should be utilised to make critical decisions for vector surveillance and controlling Kala-azar disease vectors.

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

New vineyard cultivation practices create patchy ground vegetation, favouring Woodlarks

Intensive agriculture, in which detrimental farming practices lessen food abundance and/or reduce food accessibility for many animal species, has led to a widespread collapse of farmland biodiversity. Vineyards in central and southern Europe are intensively cultivated; though they may still harbour several rare plant and animal species, they remain little studied. Over the past decades, there has been a considerable reduction in the application of insecticides in wine production, with a progressive shift to biological control (integrated production) and, to a lesser extent, organic production. Spraying of herbicides has also diminished, which has led to more vegetation cover on the ground, although most vineyards remain bare, especially in southern Europe. The effects of these potentially positive environmental trends upon biodiversity remain mostly unknown as regards vertebrates. The Woodlark (Lullula arborea) is an endangered, short-distance migratory bird that forages and breeds on the ground. In southern Switzerland (Valais), it occurs mostly in vineyards. We used radiotracking and mixed effects logistic regression models to assess Woodlark response to modern vineyard farming practices, study factors driving foraging micro-habitat selection, and determine optimal habitat profile to inform management. The presence of ground vegetation cover was the main factor dictating the selection of foraging locations, with an optimum around 55% at the foraging patch scale. These conditions are met in integrated production vineyards, but only when grass is tolerated on part of the ground surface, which is the case on ca. 5% of the total Valais vineyard area. In contrast, conventionally managed vineyards covering a parts per thousand yen95% of the vineyard area are too bare because of systematic application of herbicides all over the ground, whilst the rare organic vineyards usually have a too-dense sward. The optimal mosaic with ca. 50% ground vegetation cover is currently achieved in integrated production vineyards where herbicide is applied every second row. In organic production, ca. 50% ground vegetation cover should be promoted, which requires regular mechanical removal of ground vegetation. These measures are likely to benefit general biodiversity in vineyards.

Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images

A discussion is presented of daytime sky imaging and techniques that may be applied to the analysis of full-color sky images to infer cloud macrophysical properties. Descriptions of two different types of skyimaging systems developed by the authors are presented, one of which has been developed into a commercially available instrument. Retrievals of fractional sky cover from automated processing methods are compared to human retrievals, both from direct observations and visual analyses of sky images. Although some uncertainty exists in fractional sky cover retrievals from sky images, this uncertainty is no greater than that attached to human observations for the commercially available sky-imager retrievals. Thus, the application of automatic digital image processing techniques on sky images is a useful method to complement, or even replace, traditional human observations of sky cover and, potentially, cloud type. Additionally, the possibilities for inferring other cloud parameters such as cloud brokenness and solar obstruction further enhance the usefulness of sky imagers

Validation of and comparison between a semidistributed rainfall-runoff hydrological model (PREVAH) and a spatially distributed snow-evolution model (SnowModel) for snow cover prediction in mountain ecosystems

Snow cover is an important control in mountain environments and a shift of the snow-free period triggered by climate warming can strongly impact ecosystem dynamics. Changing snow patterns can have severe effects on alpine plant distribution and diversity. It thus becomes urgent to provide spatially explicit assessments of snow cover changes that can be incorporated into correlative or empirical species distribution models (SDMs). Here, we provide for the first time a with a lower overestimation comparison of two physically based snow distribution models (PREVAH and SnowModel) to produce snow cover maps (SCMs) at a fine spatial resolution in a mountain landscape in Austria. SCMs have been evaluated with SPOT-HRVIR images and predictions of snow water equivalent from the two models with ground measurements. Finally, SCMs of the two models have been compared under a climate warming scenario for the end of the century. The predictive performances of PREVAH and SnowModel were similar when validated with the SPOT images. However, the tendency to overestimate snow cover was slightly lower with SnowModel during the accumulation period, whereas it was lower with PREVAH during the melting period. The rate of true positives during the melting period was two times higher on average with SnowModel with a lower overestimation of snow water equivalent. Our results allow for recommending the use of SnowModel in SDMs because it better captures persisting snow patches at the end of the snow season, which is important when modelling the response of species to long-lasting snow cover and evaluating whether they might survive under climate change.

A Method for Sky-Condition Classification from Ground-Based Solar Radiation Measurements

Identification of clouds from satellite images is now a routine task. Observation of clouds from the ground, however, is still needed to acquire a complete description of cloud conditions. Among the standard meteorologicalvariables, solar radiation is the most affected by cloud cover. In this note, a method for using global and diffuse solar radiation data to classify sky conditions into several classes is suggested. A classical maximum-likelihood method is applied for clustering data. The method is applied to a series of four years of solar radiation data and human cloud observations at a site in Catalonia, Spain. With these data, the accuracy of the solar radiation method as compared with human observations is 45% when nine classes of sky conditions are to be distinguished, and it grows significantly to almost 60% when samples are classified in only five different classes. Most errors are explained by limitations in the database; therefore, further work is under way with a more suitable database

Comparison of MODIS broadband albedo over an agricultural site with ground measurements and values derived from Earth observation data at a range of spatial scales

Land surface albedo is dependent on atmospheric state and hence is difficult to validate. Over the UK persistent cloud cover and land cover heterogeneity at moderate (km-scale) spatial resolution can also complicate comparison of field-measured albedo with that derived from instruments such as the Moderate Resolution Imaging Spectrometer (MODIS). A practical method of comparing moderate resolution satellite-derived albedo with ground-based measurements over an agricultural site in the UK is presented. Point measurements of albedo made on the ground are scaled up to the MODIS resolution (1 km) through reflectance data obtained at a range of spatial scales. The point measurements of albedo agreed in magnitude with MODIS values over the test site to within a few per cent, despite problems such as persistent cloud cover and the difficulties of comparing measurements made during different years. Albedo values derived from airborne and field-measured data were generally lower than the corresponding satellite-derived values. This is thought to be due to assumptions made regarding the ratio of direct to diffuse illumination used when calculating albedo from reflectance. Measurements of albedo calculated for specific times fitted closely to the trajectories of temporal albedo derived from both Systeme pour l'Observation de la Terre (SPOT) Vegetation (VGT) and MODIS instruments.

A comparative study of satellite and ground-based phenology

Long time series of ground-based plant phenology, as well as more than two decades of satellite-derived phenological metrics, are currently available to assess the impacts of climate variability and trends on terrestrial vegetation. Traditional plant phenology provides very accurate information on individual plant species, but with limited spatial coverage. Satellite phenology allows monitoring of terrestrial vegetation on a global scale and provides an integrative view at the landscape level. Linking the strengths of both methodologies has high potential value for climate impact studies. We compared a multispecies index from ground-observed spring phases with two types (maximum slope and threshold approach) of satellite-derived start-of-season (SOS) metrics. We focus on Switzerland from 1982 to 2001 and show that temporal and spatial variability of the multispecies index correspond well with the satellite-derived metrics. All phenological metrics correlate with temperature anomalies as expected. The slope approach proved to deviate strongly from the temporal development of the ground observations as well as from the threshold-defined SOS satellite measure. The slope spring indicator is considered to indicate a different stage in vegetation development and is therefore less suited as a SOS parameter for comparative studies in relation to ground-observed phenology. Satellite-derived metrics are, however, very susceptible to snow cover, and it is suggested that this snow cover should be better accounted for by the use of newer satellite sensors.

The influence of plant species number on productivity, ground coverage and floral performance in grass-free lawns

The grass-free lawn is a novel development in modern ornamental horticulture where the traditional monoculture of grass is replaced by a variety of mowing-tolerant clonal forbs. It brings floral aesthetics and a diverse species approach to the use of lawn space. How the number of constituent forb species affects the aesthetic and structural performance of grass-free lawns was investigated using grass-free lawns composed of four, six and twelve British native clonal perennial forb species. Lawn productivity was seen to increase with increasing species number but the relationship was not linear. Plant cover was dynamic in all lawn types, varied between years and was not representative of individual species' floral performance. The behaviour of component species common to all lawns suggested that lawns with 12 species show greater structural stability than the lawns with a lower species number. Visual performance in lawns with the greatest species number was lower than in lawns with fewer species, with increasing variety in floral size and individual species floral productivity leading to a trade-off between diversity and floral performance. Individual species were seen to have different aesthetic functions in grass-free lawns either by providing flowers, ground coverage or both.