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528 results for “Land cover”
Data from: Landscape-specific thresholds in the relationship between species richness and natural land cover
1. Thresholds in the relationship between species richness and natural land cover can inform landscape-level vegetation protection and restoration targets. However, landscapes differ considerably in composition and other environmental attributes. If the effect of natural land cover on species richness depends on (i.e. interacts with) these attributes, and this affects the value of thresholds in this relationship, such dependencies must be considered when using thresholds to guide landscape management. 2. We hypothesised that the amount of natural land cover at which a threshold occurs would differ in predictable ways with particular anthropogenic, abiotic and biotic attributes of landscapes. To test this, we related woodland bird species richness in 251 landscapes, each 100 km 2, to natural land cover in south-east Australia. We compared the fit of exponential and threshold models of the richness-natural land cover relationship, focussing on the extent of natural land cover at which thresholds presented among landscapes that differed in matrix land use intensity, heterogeneity, productivity and the prevalence of strong biotic interactors. We used linear mixed modelling to examine how interactions between natural land cover and the various landscape attributes affected the fit of models of species richness. 3. Threshold models of the richness-natural land cover relationship were always a better fit than exponential models. Threshold values did not vary consistently with specific landscape attributes, with the exception of landscapes that were classified by the prevalence of strong biotic interactors (hypercompetitive native birds of the genus Manorina). 4. Natural land cover had a more positive effect on species richness in landscapes when Manorina prevalence was higher. This positive interaction provided the biggest improvement in explanatory power of models of species richness. 5. Synthesis and applications. While we detected an interaction between Manorina prevalence and the area of natural land cover, generalities relating to the underlying nature of thresholds in the richness-natural land cover relationship remain elusive. Complex interactions, relating to various landscape attributes and associated ecological processes, likely underpin variation in threshold values. Until these complexities are better understood, the use of thresholds for informing landscape management and conservation target setting should be approached with caution.
Data from: Cover crops in arable lands increase functional complementarity and redundancy of bacterial communities
1. Reducing the deleterious effects of intensive tillage and fertilisation on ecosystem integrity and human health is challenging for sustainable agriculture. The use of cover crops has been advocated as a suitable technique for this purpose, but scientific evidence to support this has been scarce. 2. After four years and a complete rotation; including wheat, maize and green pea as main crops in a ploughing system, we investigated the respective and combined effects of cover crops and nitrogen fertilisation on soil chemical and biological properties using a controlled experiment combining soil chemical analyses, high-throughput sequencing and community level physiological profiles. 3. Cover crops impeded the soil carbon and nitrogen depletion induced by intensive tillage, not only in the topsoil but also within deeper soil horizons, where more specialized bacterial communities established. 4. Cover crops induced a significant shift in soil bacterial community diversity and composition, which was associated with changes in soil chemical features and bacterial metabolic activities along the entire soil profile. 5. Cover crops enhanced soil resilience to nitrogen fertilisation by increasing functional redundancy and complementarity within soil bacterial communities and across soil horizons. 6. Synthesis and applications. In the ploughing systems commonly used for intensive agriculture in Western Europe, the use of cover crops fosters a high functional diversity among soil bacteria and thus can help to achieve a more sustainable agriculture by reducing nitrogen fertilization while maintaining yields.
Data from: Quantifying and modelling decay in forecast proficiency indicates the limits of transferability in land-cover classification
1. The ability to provide reliable projections for the current and future distribution patterns of land-covers is fundamental if we wish to protect and manage our diminishing natural resources. Two inter-related revolutions made map productions feasible at unprecedented resolutions- the availability of high-resolution remotely-sensed data and the development of machine-learning algorithms. However, the ground-truth data needed for training models is in most cases spatially and temporally clustered. Therefore, map production requires extrapolation of models from one place to another and the uncertainty cost of such extrapolation is rarely explored. In other words, we focus mainly on projections, and less on quantifying how reliable they are. 2. Following the concept of 'forecast horizon', we suggest that the predictability of land-cover classification models should be methodologically explored with quantitative tools as a continuum against distances measured along multiple dimensions. Focusing on ten agricultural sites from England and using models specifically designed to predict multivariate decay-curves we ask: how does a model's predictive performance decay with distance? More specifically, we explored if we could predict the proficiency (kappa statistics) of a model trained in one site when making predictions in another site based on the spatial, temporal, spectral and environmental distances between sites. 3. We found that model proficiency decays with spatial, temporal, spectral and environmental distance between sites. More importantly, we found for the first time that it is possible to predict the performance a model transferred to or from a novel site will have, based on its distances from known sites. The spatial distance variables where the most important when predicting model transferability. 4. Exploring model transferability as a continuum may have multiple usages including predicting uncertainty values in space and time, prioritization of strategies for ground-truth data collection, and optimizing model characteristics for defined tasks.
Mapping the páramo land cover in the Northern Andes: Figure S4 Expert land-cover classification of the Andean páramo and distribution according to three groups: natural vegetation, natural abiotic and anthropogenic, and 12 classes
<p>The Andean páramo is a biodiverse and vulnerable tropical high-mountain region, whose spatio-ecological patterns remain understudied. The lack of general characterization of its overall extent, land-cover classes, and treeline spatial features hinders our capacity to understand its responses to human impacts and predict future land-system changes. To address this knowledge gap, we classified the land-cover of the páramo in the northern Andes. Moreover, we estimated 1) the páramo's total extent and distribution among countries, 2) the relative extent of 12 of its main land-cover classes, categorized into <i>natural vegetation, natural abiotic</i> and <i>anthropogenic </i>groups, and 3) the preliminary position and anthropogenic influence of its bordering treeline. Relying on Landsat 8 imagery, we performed hybrid manual-automated classifications using the Maximum Likelihood and Random Forest algorithms. The two resulting <i>final classifications</i> were manually checked for errors compared to Google Earth and VegPáramo data, and used to produce the <i>expert classification</i>. Finally, we delimited the treeline based on regional forest connectivity, and applied it to the expert classification to evaluate páramo elevations, surface areas and land-cover classes above the treeline. The páramo extent was estimated at 24,301 km<sup>2</sup>, distributed between Ecuador (47%), Colombia (43%), Venezuela (8%) and Peru (2%). Natural vegetation, especially shrublands, rosette plant communities and grasslands were dominant (altogether, 65%), whereas classes reflecting intense land-use covered 12% overall. The average treeline reached 3546 m and was bordered uphill at 16% with anthropogenic land-cover classes. The páramo's extent is smaller than previously suggested. It remains a (semi-) natural region, yet crop and pasture expansion towards high elevations is a critical concern for long-term sustainability. Future research can build on our findings to predict land-system changes and assess priority areas for conservation. We recommend for future research to focus on remnant forest patches and treeline connectivity in priority.</p>
Land use and land cover in China for WRF (Resolution: 500m), 2020
<p>WRF使用的中国2020年下垫面数据。</p>
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
<p><strong>Project Page</strong></p> <p><a href="https://open-earth-map.org/">https://open-earth-map.org/</a></p> <p><strong>Paper</strong></p> <p><a href="https://arxiv.org/abs/2210.10732">https://arxiv.org/abs/2210.10732</a></p> <p><strong>Overview</strong></p> <p>OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. OpenEarthMap consists of 5000 aerial and satellite images with manually annotated 8-class land cover labels and 2.2 million segments at a 0.25-0.5m ground sampling distance, covering 97 regions from 44 countries across 6 continents. OpenEarthMap fosters research including but not limited to semantic segmentation and domain adaptation. Land cover mapping models trained on OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications.</p> <p><strong>Reference</strong></p> <pre><code>@inproceedings{xia_2023_openearthmap, title = {OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping}, author = {Junshi Xia and Naoto Yokoya and Bruno Adriano and Clifford Broni-Bediako}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6254-6264} }</code></pre> <p><strong>License</strong></p> <p>Label data of OpenEarthMap are provided under the same license as the original RGB images, which varies with each source dataset. For more details, please see the attribution of source data <a href="https://open-earth-map.org/attribution.html">here</a>. Label data for regions where the original RGB images are in the public domain or where the license is not explicitly stated are licensed under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0</a> International License.</p> <p><strong>Note for xBD data</strong></p> <p>The RGB images of xBD dataset are not included in the OpenEarthMap dataset. Please download the xBD RGB images from <a href="https://xview2.org/dataset">https://xview2.org/dataset</a> and add them to the corresponding folders. The "xbd_files.csv" contains information about how to prepare the xBD RGB images and add them to the corresponding folders.</p> <p><strong>Code</strong></p> <p>Sample code to add the xBD RGB images to the distributed OpenEarthMap dataset and to train baseline models is available <a href="https://github.com/bao18/open_earth_map">here</a>.</p> <p><strong>Leaderboard</strong></p> <p>Performance on the test set can be evaluated on the <a href="https://codalab.lisn.upsaclay.fr/competitions/9121">Codalab webpage</a>.</p>
Supplementary File 3; Figures S3.1 to S3.3: Additional photographs, complementing those presented in figure 2, to illustrate different ways in which signs of livestock, humans or wild mammal activity, as well as land cover attributes, were observed and classified
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Figure 1 in Spatial distribution and effects of land use and cover on cutaneous leishmaniasis vectors in the municipality of Paracambi, Rio de Janeiro, Brazil
Figure 1 Phlebotomine sand fly capture sites, land use, and land cover in the municipality of Paracambi, Rio de Janeiro State, Brazil. Detail below: example of the extraction of the annual percentages of land use and land cover in a 200-meter buffer in one of the capture sites (1992-1994 and 2001-2003). Spatial resolution of the land cover data is 30 meters (Source: mapbiomas.org).
Figure 3 in Spatial distribution and effects of land use and cover on cutaneous leishmaniasis vectors in the municipality of Paracambi, Rio de Janeiro, Brazil
Figure 3 Kernel density map of the total number of sand flies captured in Paracambi, RJ, Brazil, 1992-1994 and 2001-2003.
Data and calculations associated with "Tracking cropland transitions: a comparative analysis of U.S. land cover change data"
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A dataset of land cover samples over the Tibetan Plateau
<p>A dataset consists of 10,242 land cover samples over the Tibetan Plateau with 12 vegetation types and 3 non-vegetation types created through manual interpretation and field trips.</p>
Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA
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Data from: Landscape-specific thresholds in the relationship between species richness and natural land cover
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Data from: Cover crops in arable lands increase functional complementarity and redundancy of bacterial communities
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Analysis of land cover evolution within the built-up areas of provincial capital cities in northeastern China based on nighttime light data and Landsat data
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Mapping the páramo land cover in the Northern Andes: Figure S4 Expert land-cover classification of the Andean páramo and distribution according to three groups: natural vegetation, natural abiotic and anthropogenic, and 12 classes
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Data from: Quantifying and modelling decay in forecast proficiency indicates the limits of transferability in land-cover classification
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Similarity between agricultural and natural land covers shapes how biodiversity responds to agricultural expansion at landscape scales
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Effects of land cover and water availability on brittlebush (Encelia farinosa) flowering phenology and its pollinator community.
Phenology is the seasonal timing of environment-mediated events such as growth and reproduction. Phenology is quantified by determining time of onset and end of events, duration, and number of flowers (Augspurger 1983, Rathcke and Lacey 1985). Studies of flowering and leafing phenology have dramatically increased during the last few decades due to growing concerns over global climate change and because phenology is a highly sensitive indicator that researchers can use to study the effects of climate change at multiple scales (Chuine et al. 2000, Sparks and Menzel 2002, Peuelas et al. 2004, Williams and Abberton 2004). Urban climatic conditions are considered similar to the changing global climate conditions; therefore, many researchers study urbanized areas as smaller scale experiments, or models, of global climate change (Ziska et al. 2003). Concerns over climate change are not the only reasons for studying urban ecosystems. It is important to create urban environments resilient to social, economic, and ecological collapse. The literature of flowering phenology in urban environments suggests that spring-blooming plants in urban environments located in temperate, Mediterranean, and boreal ecosystems in North America, Europe, and China tend to bloom earlier in the city than in the surrounding un-urbanized habitat (Roetzer et al. 2000, Fitter and Fitter 2002, White et al. 2002, Ziska et al. 2003, Zhang et al. 2004). Moreover, non-woody plants, early spring bloomers, and insect-pollinated plants in these environments tend to be more sensitive than woody plants, mid- or late-spring bloomers, and wind-pollinated plants (Fitter and Fitter 2002, Traidl-Hoffman et al. 2003). Finally, temperature (Heat Island Effect) has been assumed to be the cause of earlier flowering in the urban environments since the large-scale advancement of flowering has been strongly correlated with global warming. Study of flowering phenology in urban ecosystems is important because changes in phen
Land cover classification of Central Arizona-Phoenix using Landsat Thematic Mapper (TM) data - year 1985
Land cover classification for the CAP LTER study region using Landsat Thematic Mapper (TM) data - year 1985
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.