Damage by insects pests to the Djingarey Ber Mosque in Timbuktu: detection and control

The Djingarey Ber Mosque in Timbuktu (Mali) is one of the most significant earthen construction in West Africa. Originally constructed in 1327, it was included in 1988 on the World Heritage UNESCO List for its unique architecture and historical importance. During its restoration, recently undertaken by the Aga Khan Trust for Culture, the wooden parts of the roof and architraves showed clear signs of threatening insect presence. In order to identify the pests responsible of the damage, evaluate its extent and suggest a proper control strategy, a detailed survey was performed inside the Mosque complex and in its immediate surroundings. The entomological inspection, performed in the dry-cold season, allowed to detect signs of insect damage in most of the wooden elements, even in the recently replaced beams, but also in walls, pillars and the precious decorated panels. Damages in the wood elements could be attributed to Amitermes evuncifer Silvestri (Termitidae), Bostrychoplites zycheli Marseuli (Bostrichidae) and Lyctus africanus Lesne (Lyctidae), which were collected alive on site. Injures in the walls and decorated panels appeared to be performed by hymenopterans such as \u201cplasterer bees\u201d (Colletidae) and Sphecidae. From the evaluation of the type and extent of damage in relation to the architecture and materials used in its construction and decoration, the most serious pest and the worse threat for the mosque is represented by termites. Control and preventive measures, in the view of a sustainable, long-lasting integrated management are suggested

The Most Detailed Portrait of Earth

The most detailed maps ever of Earth¿s land surface have been created with the help of ESA¿s Envisat environmental satellite. Land cover has been charted from space before, but this global map has a resolution 10 times sharper than any of its predecessors.JRC.H.3-Global environement monitorin

Climate-Related Hazards: A Method for Global Assessment of Urban and Rural Population Exposure to Cyclones, Droughts, and Floods

Global climate change (GCC) has led to increased focus on the occurrence of, and preparation for, climate-related extremes and hazards. Population exposure, the relative likelihood that a person in a given location was exposed to a given hazard event(s) in a given period of time, was the outcome for this analysis. Our objectives were to develop a method for estimating the population exposure at the country level to the climate-related hazards cyclone, drought, and flood; develop a method that readily allows the addition of better datasets to an automated model; differentiate population exposure of urban and rural populations; and calculate and present the results of exposure scores and ranking of countries based on the country-wide, urban, and rural population exposures to cyclone, drought, and flood. Gridded global datasets on cyclone, drought and flood occurrence as well as population density were combined and analysis was carried out using ArcGIS. Results presented include global maps of ranked country-level population exposure to cyclone, drought, flood and multiple hazards. Analyses by geography and human development index (HDI) are also included. The results and analyses of this exposure assessment have implications for country-level adaptation. It can also be used to help prioritize aid decisions and allocation of adaptation resources between countries and within a country. This model is designed to allow flexibility in applying cyclone, drought and flood exposure to a range of outcomes and adaptation measures

Inter-comparison of satellite sensor land surface phenology and ground phenology in Europe

Land surface phenology (LSP) and ground phenology (GP) are both important sources of information for monitoring terrestrial ecosystem responses to climate changes. Each measures different vegetation phenological stages and has different sources of uncertainties, which make comparison in absolute terms challenging, and therefore, there has been limited attempts to evaluate the complementary nature of both measures. However, both LSP and GP are climate driven and therefore should exhibit similar interannual variation. LSP obtained from the whole time series of Medium-Resolution Imaging Spectrometer data was compared to thousands of deciduous tree ground phenology records of the Pan European Phenology network (PEP725). Correlations observed between the interannual time series of the satellite sensor estimates of phenology and PEP725 records revealed a close agreement (especially for Betula Pendula and Fagus Sylvatica species). In particular, 90% of the statistically significant correlations between LSP and GP were positive (mean R2 = 0.77). A large spatiotemporal correlation was observed between the dates of the start of season (end of season) from space and leaf unfolding (autumn coloring) at the ground (pseudo R2 of 0.70 (0.71)) through the application of nonlinear multivariate models, providing, for the first time, the ability to predict accurately the date of leaf unfolding (autumn coloring) across Europe (root-mean-square error of 5.97 days (6.75 days) over 365 days)

Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe

This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 124, 321-333.DOI :10.1016/j.rse.2012.05.024.The remote sensing measurement of land surface temperature from satellites provides a monitoring of this magnitude on a continuous and regular basis, which is a critical factor in many research fields such as weather forecasting, detection of forest fires or climate change studies, for instance. The main problem of measuring temperature from space is the need to correct for the effects of the atmosphere and the surface emissivity. In this work an automatic procedure based on the Vegetation Cover Method, combined with the GLOBCOVER land surface type classification, is proposed. The algorithm combines this land cover classification with remote sensing information on the vegetation cover fraction to obtain land surface emissivity maps for AATSR split-window bands. The emissivity estimates have been compared with ground measurements in two validation cases in the area of rice fields of Valencia, Spain, and they have also been compared to the classification-based emissivity product provided by MODIS (MOD11_L2). The results show that the error in emissivity of the proposed methodology is of the order of ±0.01 for most of the land surface classes considered, which will contribute to improve the operational land surface temperature measurements provided by the AATSR instrument. © 2012 Elsevier Inc. All rights reserved.This work was funded by the Generalitat Valenciana (project PRO-METEO/2009/086, and contract of Eduardo Caselles) and the Spanish Ministerio de Ciencia e Innovacion (projects CGL2007-64666/CLI, CGL2010-17577/CLI and CGL2007-29819-E, co-financed with FEDER funds). AATSR data were provided by European Space Agency (ESA) under Cat-1 project 3466. We also thank ESA and the ESA GLOBCOVER Project, led by MEDIAS-France, for the GLOBCOVER classification data. The comments and suggestions of three anonymous reviewers that improved the paper are also acknowledged.Caselles, E.; Valor, E.; Abad Cerdá, FJ.; Caselles, V. (2012). Automatic classification-based generation of thermal infrared land surface emissivity maps using AATSR data over Europe. Remote Sensing of Environment. 124:321-333. https://doi.org/10.1016/j.rse.2012.05.024S32133312

Perennial snow and ice variations (2000–2008) in the Arctic circumpolar land area from satellite observations

Perennial snow and ice (PSI) extent is an important parameter of mountain environments with regard to its involvement in the hydrological cycle and the surface energy budget. We investigated interannual variations of PSI in nine mountain regions of interest (ROI) between 2000 and 2008. For that purpose, a novel MODIS data set processed at the Canada Centre for Remote Sensing at 250 m spatial resolution was utilized. The extent of PSI exhibited significant interannual variations, with coefficients of variation ranging from 5% to 81% depending on the ROI. A strong negative relationship was found between PSI and positive degree‐days (threshold 0°C) during the summer months in most ROIs, with linear correlation coefficients (r) being as low as r = −0.90. In the European Alps and Scandinavia, PSI extent was significantly correlated with annual net glacier mass balances, with r = 0.91 and r = 0.85, respectively, suggesting that MODIS‐derived PSI extent may be used as an indicator of net glacier mass balances. Validation of PSI extent in two land surface classifications for the years 2000 and 2005, GLC‐2000 and Globcover, revealed significant discrepancies of up to 129% for both classifications. With regard to the importance of such classifications for land surface parameterizations in climate and land surface process models, this is a potential source of error to be investigated in future studies. The results presented here provide an interesting insight into variations of PSI in several ROIs and are instrumental for our understanding of sensitive mountain regions in the context of global climate change assessment

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits.We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics.More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species.Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species.Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change

Scaling up Semi-Arid Grassland Biochemical Content from the Leaf to the Canopy Level: Challenges and Opportunities

Remote sensing imagery is being used intensively to estimate the biochemical content of vegetation (e.g., chlorophyll, nitrogen, and lignin) at the leaf level. As a result of our need for vegetation biochemical information and our increasing ability to obtain canopy spectral data, a few techniques have been explored to scale leaf-level biochemical content to the canopy level for forests and crops. However, due to the contribution of non-green materials (i.e., standing dead litter, rock, and bare soil) from canopy spectra in semi-arid grasslands, it is difficult to obtain information about grassland biochemical content from remote sensing data at the canopy level. This paper summarizes available methods used to scale biochemical information from the leaf level to the canopy level and groups these methods into three categories: direct extrapolation, canopy-integrated approach, and inversion of physical models. As for semi-arid heterogeneous grasslands, we conclude that all methods are useful, but none are ideal. It is recommended that future research should explore a systematic upscaling framework which combines spatial pattern analysis, canopy-integrated approach, and modeling methods to retrieve vegetation biochemical content at the canopy level

Effects of urbanization on precipitation in Beijing

Since the 1980s, the industrialization and urbanization of the Beijing area has entered a period of high-speed growth. This paper asks the question: How have such great changes in urban land-use over the past decades impacted urban precipitation? In this study, we investigate and analyze the effects of urbanization on the summer precipitation in Beijing using numerical modeling approaches. Applying the numerical mesoscale atmospheric model METRAS, we determine the impact of surface cover on 13 heavy precipitation events. We implement five idealized land-use scenarios: Reference scenario, No-urban scenario, High-building scenario, Urban-expand scenario, and No-vegetation scenario. There is nearly no difference in the mean precipitation sum across all 13 simulated rain events and between the urban-scenarios and the rural-scenario. We find effects of urbanization on precipitation only in some single cases. We conclude urbanization does effect the local precipitation of Beijing; it reduces rainfall in the urban area and increases rainfall downwind of the city. In some cases, larger percentage of sealed area could give rise to the heavier precipitation or extreme rain events. And we conclude the urban pattern significantly impacts rainfall area and intensity. Increased urban size or density may speed up rain clouds while increased urban height may disrupt or bifurcate the clouds. Our results offer a new viewpoint and further the study of urban impacts on precipitation (UIP). The results are important for sustainable and harmonious development of the economy, society, and environment in Beijing as well as other cities with rapid urbanization