Paris, France, 1422-1589 (Time Series Map 6 of 8) (Raster Image)

This layer is a georeferenced raster image from the historic paper map series entitled: Lutece ... plan de la ville de Paris ..., par M.L.C.D.L.M. ; A. Coquart, delineavit et sculp. It was published by Jean & Pierre Cot in 1705. Scale [ca. 1:10,000]. This image is of map 6 entitled: Sixiême plan de la ville de Paris et ses accroissements depuis le commencement du régne de Charles VII. l'an 1422 jusqu'a la fin du régne d'Henry III. l'an 1589: tiré des lettres patentes qui ont ordonné les ouvrages, des contrats passez avec les entrepreneurs, des registres de la chambre des comptes de l'histoire et des memoires du temps. The map represents Paris, 1422 to 1589. Map in French.The image inside the map neatline is georeferenced to the surface of the earth and fit to the European Datum 1950, Universal Transverse Mercator (UTM) Zone 31N projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as drainage, towns and villages, roads, built-up areas and selected buildings, fortification, ground cover, and more. Relief shown by hachures. Includes index, text, and notes.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

Kyoto, Japan, 1881 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Kyoto-ku kumiwake saizu, Inagaki Ryozo henshu. It was published by Imai Shichirobee, in 1881. Scale [ca. 1:13,000]. Covers Kyoto, Japan. Map in Japanese. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Tokyo Universal Transverse Mercator (UTM) Zone 53N projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as roads, railroads, drainage, built-up areas and selected buildings, fortifications, ground cover, and more. Relief shown pictorially. Includes distance chart, index, and legend.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

Southern Africa, 1897 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Juta's map of South Africa from the Cape to the Zambesi, compiled from the best available Colonial and Imperial information including the official Cape Colony map by the Surveyor General, Cape Town, Dr. T. Hahn's Damaraland, and F.C. Selous' journals and sketches ; published by J.C. Juta & Co. It was published by J.C. Juta : Edward Stanford. in 1897. Scale [ca. 1:2,534,400]. Covers Southern Africa. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Africa Lambert Conformal Conic projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as drainage, cities and other human settlements, territorial and administrative boundaries, shoreline features, roads, railroads, mines, and more. Relief shown by hachures. Includes also notes about land cover.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

Chester Castle, Chester, England, ca. 1745 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: A plan of the castle of Chester : with additional new works errected by order of ... the Earl of Cholmondeley, whereunto is added a project of four bastions in order to defend the antient walls against a regular seige ... by ... Alexander de Lavaux. R. Parr sculp. It was published by Alexander de Lavaux ca. 1745. Scale [ca. 1:300]. The image inside the map neatline is georeferenced to the surface of the earth and fit to the 'British National Grid' coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows fortification features such as buildings, stables, guard houses, towers, quarters, stairs, gates, parade grounds, arsenals, wells, ground cover, and more. Includes also index.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

North & West Africa, 1647 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Nova Barbariae descriptio. It was published by Apud Ioannem Ianssonium in 1647. Scale [ca. 1:12,100,000]. Cover North & West Africa. Map in Latin.The image inside the map neatline is georeferenced to the surface of the earth and fit to the Africa Lambert Conformal Conic projected coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as drainage, cities and other human settlements, territorial boundaries, shoreline features, and more. Relief shown pictorially. Includes also notes.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

Chester, England, ca. 1745 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Plan of the city & castle of Chester, survey'd and drawn by Alexander De Lavaux, engineer ; R. Parr sculp. It was published ca. 1745. Scale [ca. 1:3,960]. The image inside the map neatline is georeferenced to the surface of the earth and fit to the 'British National Grid' coordinate system. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as roads, drainage, built-up areas and selected buildings, fortification, ground cover, and more. Relief shown by hachures.This layer is part of a selection of digitally scanned and georeferenced historic maps from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of originators, ground condition dates, scales, and map purposes.

Downscaled land surface temperatures obtained by a particle smoothing approach, with links to NetCDF files

This dataset contains continuous time series of land surface temperature (LST) at spatial resolution of 300m around the 12 experimental sites of the PAGE21 project (grant agreement number 282700, funded by the EC seventh Framework Program theme FP7-ENV-2011). This dataset was produced from hourly LST time series at 25km scale, retrieved from SSM/I data (André et al., 2015, doi:10.1016/j.rse.2015.01.028) and downscaled to 300m using a dynamic model and a particle smoothing approach. This methodology is based on two main assumptions. First, LST spatial variability is mostly explained by land cover and soil hydric state. Second, LST is unique for a land cover class within the low resolution pixel. Given these hypotheses, this variable can be estimated using a land cover map and a physically based land surface model constrained with observations using a data assimilation process. This methodology described in Mechri et al. (2014, doi:10.1002/2013JD020354) was applied to the ORCHIDEE land surface model (Krinner et al., 2005, doi:10.1029/2003GB002199) to estimate prior values of each land cover class provided by the ESA CCI-Land Cover product (Bontemps et al., 2013) at 300m resolution . The assimilation process (particle smoother) consists in simulating ensemble of LST time series for each land cover class and for a large number of parameter sets. For each parameter set, the resulting temperatures are aggregated considering the grid fraction of each land cover and compared to the coarse observations. Miniminizing the distance between the aggregated model solutions and the observations allow us to select the simulated LST and the corresponding parameter sets which fit the observations most closely. The retained parameter sets are then duplicated and randomly perturbed before simulating the next time window. At the end, the most likely LST of each land cover class are estimated and used to reconstruct LST maps at 300m resolution using ESA CCI-Land Cover. The resulting temperature maps on which ice pixels were masked, are provided at daily time step during the nine-year analysis period (2000-2009).

Characteristics of soil profiles from the Ouémé Upper Catchment (HVO), Térou, Benin

Soil degradation threatens agricultural production and food security in Sub-Saharan Africa. In the coming decades, soil degradation, in particular soil erosion, will become worse through the expansion of agriculture into savannah and forest and changes in climate. This study aims to improve the understanding of how land use and climate change affect the hydrological cycle and soil erosion rates at the catchment scale. We used the semi-distributed, time-continuous erosion model SWAT (Soil Water Assessment Tool) to quantify runoff processes and sheet and rill erosion in the Upper Ouémé River catchment (14500 km**2, Central Benin) for the period 1998-2005. We could then evaluate a range of land use and climate change scenarios with the SWAT model for the period 2001-2050 using spatial data from the land use model CLUE-S and the regional climate model REMO. Field investigations were performed to parameterise a soil map, to measure suspended sediment concentrations for model calibration and validation and to characterise erosion forms, degraded agricultural fields and soil conservation practices. Modelling results reveal current "hotspots" of soil erosion in the north-western, eastern and north-eastern parts of the Upper Ouémé catchment. As a consequence of rapid expansion of agricultural areas triggered by high population growth (partially caused by migration) and resulting increases in surface runoff and topsoil erosion, the mean sediment yield in the Upper Ouémé River outlet is expected to increase by 42 to 95% by 2025, depending on the land use scenario. In contrast, changes in climate variables led to decreases in sediment yield of 5 to 14% in 2001-2025 and 17 to 24% in 2026-2050. Combined scenarios showed the dominance of land use change leading to changes in mean sediment yield of -2 to +31% in 2001-2025. Scenario results vary considerably within the catchment. Current "hotspots" of soil erosion will aggravate, and a new "hotspot" will appear in the southern part of the catchment. Although only small parts of the Upper Ouémé catchment belong to the most degraded zones in the country, sustainable soil and plant management practices should be promoted in the entire catchment. The results of this study can support planning of soil conservation activities in Benin.

Interactions between landcover pattern and geospatial processing methods:effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric "number of patches" than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty. © 2013 Elsevier B.V.

A tapestry of land uses and land covers typify our study site. Here, a commercial farm surrounds a new residential development.

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Ecological Homogenization of Urban USA

A visually apparent but scientifically untested outcome of land-use change is homogenization across urban areas, where neighborhoods in different parts of the country have similar patterns of roads, residential lots, commercial areas, and aquatic features. We hypothesize that this homogenization extends to ecological structure and also to ecosystem functions such as carbon dynamics and microclimate, with continental-scale implications. Further, we suggest that understanding urban homogenization will provide the basis for understanding the impacts of urban land-use change from local to continental scales. Here, we show how multi-scale, multi-disciplinary datasets from six metropolitan areas that cover the major climatic regions of the US (Phoenix, AZ; Miami, FL; Baltimore, MD; Boston, MA; Minneapolis–St Paul, MN; and Los Angeles, CA) can be used to determine how household and neighborhood characteristics correlate with land-management practices, land-cover composition, and landscape structure and ecosystem functions at local, regional, and continental scales.

Macroinvertebrate abundance in montane tropical streams of varying land-use, Ecuador

Despite the importance of tropical montane cloud forest streams, studies investigating aquatic communities in these regions are rare and knowledge on the driving factors of community structure is missing. The objectives of this study therefore were to understand how land-use influences habitat structure and macroinvertebrate communities in cloud forest streams of southern Ecuador. We evaluated these relationships in headwater streams with variable land cover, using multivariate statistics to identify relationships between key habitat variables and assemblage structure, and to resolve differences in composition among sites. Results show that shading intensity, substrate type and pH were the environmental parameters most closely related to variation in community composition observed among sites. In addition, macroinvertebrate density and partly diversity was lower in forested sites, possibly because the pH in forested streams lowered to almost 5 during spates. Standard bioindicator metrics were unable to detect the changes in assemblage structure between disturbed and forested streams. In general, our results indicate that tropical montane headwater streams are complex and heterogeneous ecosystems with low invertebrate densities. We also found that some amount of disturbance, i.e. patchy deforestation, can lead at least initially to an increase in macroinvertebrate taxa richness of these streams.

Bioenergy and climate change mitigation : an assessment

Date of Acceptance: 08/05/2014 Acknowledgements The authors are indebted to Julia Römer for assisting with editing several hundred references. Helmut Haberl gratefully acknowledges funding by the Austrian Academy of Sciences (Global Change Programme), the Austrian Ministry of Science and Research (BMWF, proVision programme) as well as by the EU-FP7 project VOLANTE. Carmenza Robledo-Abad received financial support from the Swiss State Secretariat for Economic Affairs.

ELUM: a spatial modelling tool to predict soil greenhouse gas changes from land conversion to bioenergy in the UK

Acknowledgements This work is based on the Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial (ELUM) project, which was commissioned and funded by the Energy Technologies Institute (ETI). The authors are grateful to Niall McNamara (Centre for Ecology & Hydrology, Lancaster) for coordinating the project and to Dagmar Henner (University of Aberdeen) for project assistance. We are also grateful to staff at the ETI, particularly to Geraldine Newton-Cross, Geraint Evans and Hannah Evans for constructive advice and feedback, and to Jonathan Oxley for project support. The ELUM Software Package contains Ordnance Survey data © Crown copyright and database right 2012.

Bioenergy and climate change mitigation : an assessment

Date of Acceptance: 08/05/2014 Acknowledgements The authors are indebted to Julia Römer for assisting with editing several hundred references. Helmut Haberl gratefully acknowledges funding by the Austrian Academy of Sciences (Global Change Programme), the Austrian Ministry of Science and Research (BMWF, proVision programme) as well as by the EU-FP7 project VOLANTE. Carmenza Robledo-Abad received financial support from the Swiss State Secretariat for Economic Affairs.