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115 results for “land use and land cover”
Land cover classification using Landsat Enhanced Thematic Mapper (ETM) data - year 2000
This land cover classification map was created using Landsat Enhanced Thematic Mapper (ETM) data from the year 2000. The map covers the area of the Central Arizona-Phoenix Long Term Ecological Research study.
Land cover classification of central Arizona-Phoenix using Landsat Enhanced Thematic Mapper (ETM) data, year 2005
A fundamental dataset required for ecosystem analysis consists of the major types of land cover present in the study area and their areal percentages. Land cover refers to the physical nature of the surficial materials present in a given area such as water, grass, clay-rich soil, asphalt, or concrete. Land cover classification can be used as input into a variety of ecological models, and land cover maps can be constructed to aid in planning field sampling strategy. The land cover types can also be linked to different land use categories to investigate temporal and spatial changes in the urban ecosystem.
Point Count Bird Censusing Data Subset for Paper 'EFFECTS OF LAND USE AND VEGETATION COVER ON BIRD COMMUNITIES' Walker et. al
Animals utilize their environment across a range of scales, which is bounded by their extent, the broadest spatial area which organisms respond to their environment within their lifetime, and the spatial grain, the smallest area they respond to their environment (Kotlier and Wiens 1990). Within this range, organisms likely respond to their environment at a hierarchy of levels. Johnson (1980) recognizes four distinct levels of hierarchical habitat selection. At the very largest scale, first order selection, includes the entire area that an organism utilizes within its lifetime, and is also known as an organisms global home range or extent. In contrast, second order selection is an organisms local home range, or the area that it occupies within a unique ecosystem. This distinction is most apparent with migratory animals who utilize more than one distinct landscape for their survival (i.e. summer vs. winter feeding grounds), and much less so for organisms resident of one specific landscape for their entire life span. Third order selection is the selection of specific habitat patches within an ecosystem. For example, a Monarch butterfly would tend to select patches of milkweed within a prairie. And the lowest level, fourth order selection, involves the physical procurement of food within a selected patch, in our example, specific flowers within a milkweed patch, and is also known as grain. Realizing the importance of hierarchical habitat selection, it has become apparent that single-scale studies of animals responses to their environment may fail to adequately represent how that specific animal is responding to ecological parameter of interest, especially if they are not responding to the landscape at that scale (Holling 1992). The range of scales which an animal of interest is utilizing a landscape is important to determine prior to any further ecological investigation, as inappropriate scalar mismatch between organism and environment can lead to ambiguous or even dece
Land cover classification using Landsat (MSS) data for the Central Arizona-Phoenix area - year 1979
This land cover classification map was created using Landsat MSS data from the year 1979. The map covers the area of the Central Arizona-Phoenix Long Term Ecological Research study.
Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1985
This land cover classification map was created using Landsat TM data from the year 1985. The map covers the area of the Central Arizona-Phoenix Long Term Ecological Research study.
Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1991
This land cover classification map was created using Landsat TM data from the year 1991. The map covers the area of the Central Arizona-Phoenix Long Term Ecological Research study.
Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1995
This land cover classification map was created using Landsat TM data from the year 1995. The map covers the area of the Central Arizona-Phoenix Long Term Ecological Research study.
Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1990
Land cover classification for the Central Arizona-Phoenix CAP LTER study region using Landsat Thematic Mapper (TM) data - for the year 1990
Land cover classification of the CAP LTER study area at five-year intervals from 1985 to 2010 using Landsat imagery
The project aims to facilitate the long-term environmental change monitoring and social-ecological studies regarding urban sprawl and dynamics, urban heat islands, and outdoor water consumption, etc. Six land-use/land-cover (LULC) maps at 30 m resolution are created from 1985 to 2010 at five year intervals. Systematic object-based classification is utilized to ensure the map consistency and direct comparison capability over time. In the result, 11 land-use/land-cover classes are identified with an overall accuracy of 92.1%.
Land cover classification of Central Arizona-Phoenix using Landsat Thematic Mapper (TM) data - year 1998
Land cover classification for the Central Arizona-Phoenix CAP LTER study region using Landsat Thematic Mapper (TM) data - for the year 1998
Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm
<p>This package supplements the following paper entitled “Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm” published with Science China Earth Sciences.</p>
Integrating stakeholders' perspectives and spatial modelling to develop scenarios of future land use and land cover change in northern Tanzania
<p>Rapid rates of land use and land cover change (LULCC) in eastern Africa and limited instances of genuinely equal partnerships involving scientists, communities and decision makers challenge the development of robust pathways toward future environmental and socioeconomic sustainability. We use a participatory modelling tool, Kesho, to assess the biophysical, socioeconomic, cultural and governance factors that influenced past (1959-1999) and present (2000-2018) LULCC in northern Tanzania and to simulate four scenarios of land cover change to the year 2030. Simulations of the scenarios used spatial modelling to integrate stakeholders' perceptions of future environmental change with social and environmental data on recent trends in LULCC. From stakeholders' perspectives, between 1959 and 2018, LULCC was influenced by climate variability, availability of natural resources, agriculture expansion, urbanization, tourism growth, and legislation governing land access and natural resource management. Among other socio-environmental-political LULCC drivers, the stakeholders envisioned that from 2018 to 2030 LULCC will largely be influenced by land health, natural and economic capital, and political will in implementing land use plans and policies. The projected scenarios suggest that by 2030 agricultural land will have expanded by 8-20% under different scenarios and herbaceous vegetation and forest land cover will be reduced by 2.5-5% and 10-19% respectively. Stakeholder discussions further identified desirable futures in 2030 as those with improved infrastructure, restored degraded landscapes, effective wildlife conservation, and better farming techniques. The undesirable futures in 2030 were those characterized by land degradation, poverty, and cultural loss. Insights from our work identify the implications of future LULCC scenarios on wildlife and cultural conservation and in meeting the Sustainable Development Goals (SDGs) and targets by 2030. The Kesho approach capitalizes on knowledge exchanges among diverse stakeholders, and in the process promotes social learning, provides a sense of ownership of outputs generated, democratizes scientific understanding, and improves the quality and relevance of the outputs.</p>
Data from: Wildfire activity and land use drove 20th-century changes in forest cover in the Colorado front range
Recent shifts in global forest area highlight the importance of understanding the causes and consequences of forest change. To examine the influence of several potential drivers of forest cover change, we used supervised classifications of historical (1938–1940) and contemporary (2015) aerial imagery covering a 2932‐km2 study area in the northern Front Range (NFR) of Colorado and we linked observed changes in forest cover with abiotic factors, land use, and fire history. Forest cover in the NFR demonstrated broad‐scale changes 1938–2015 and overall cover increased 7.8%, but there was notable spatial variability and many sites also experienced Forest Loss. Recent (1978–2015) wildfire was the largest single driver of Forest Loss, with fires burning 14.3% of the total study area. Recently burned areas showed net losses of 36.9% forest cover. Reasons for Forest Gain were more complex, with elevation, past mining density, fire history, and topographic heat load index being the strongest predictors of increases in forest cover. Historical mining activity is one of the dominant anthropogenic impacts in ecosystems in the NFR and it had a complex, non‐linear relationship with 20th‐century changes in forest cover. Subalpine stands originating after stand‐replacing fires circa mid‐1800s to early 1900s showed some of the greatest gains in forest cover, indicative of slow and continuous post‐fire recovery through the 20th century. We also investigated factors such as land ownership, road density, forest management activities, and development intensity, which played detectable, but more minor roles in observed change. Twentieth‐century changes in forest cover throughout the NFR are a result of ecological disturbances and anthropogenic influences operating at varying timescales and overlaid upon variability in the abiotic environment.
Best learned models : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Best learned models (Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models) for each region based on the classification data set DS-A with seed 0 (see description <a href="https://zenodo.org/deposit/7099785">here</a>). </p><p>Models: GP non spatial, GP spatial (sum), GP spatial (product),RF non spatial, RF spatial, MLP non spatial, MLP spatial, LTAE non spatial, LTAE spatial</p><p>For further details see section VI-C of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p><p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Land use and land cover in communities along the Bons Sinais estuary, Mozambique
<p><span>The Bons Sinais estuary (BSE) is one of the most important estuaries in the central region of the Mozambican coast. The BSE plays an important role as main source of food and income for many people living along the estuary. The present data contain information on the land use and cover of eight communities located along the Bons Sinais estuary, Mozambique, over the time period 2019-2020. The dataset was created by drawing shape files over high-resolution satellite imagens obtained from Bing and Google Satellites. All images were analysed using QGIS software. A total of 101 shapefiles were created. These files will help local managers and researchers to better understand the human use of the natural resources and landscape, as well as to predict impacts of the community's growth on natural resources in the BSE.</span></p>
Boundary Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Boundary data set used to evaluate the continuity predictions for different models in boundary zones. It is composed of labelled and unlabelled pixels for a boundary size of 100m and 200m.</p> <p>For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p> <p>To compute the predictions in the boundary zones with different models (GP, RF, MLP, LTAE), the code is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
CR2LUC: Historical (1950-2020) land use and land cover in continental Chile
<p>The CR2LUC product was created to track long-term land-use changes in Chile since 1950, using a method that combines historical data with satellite information. Unlike recent land use/cover estimates that rely heavily on satellite data, CR2LUC integrates various sources, including agricultural censuses and other national statistics. This reconstruction involves extensive data preprocessing, such as digitizing historical documents. Key data sources include Agricultural Censuses (1955, 1965, 1976, 1997, 2007), annual crop statistics (1997-2021), and cadastres for fruits, viticulture, and vegetation.</p> <p>This dataset has been developed within the framework of the Center for Climate and Resilience Research (CR2, ANID/FONDAP/1523A0002) and the research project ANID/FSEQ210001.</p> <p> </p>
Datasets and code used to generate the figures in the article "Influence of Forest Cover Loss on Land Surface Temperature Differs by Drivers in China"
<p>We have provided the data and code used to generate the figures in the article "Influence of Forest Cover Loss on Land Surface Temperature Differs by Drivers in China" for reference and further reading. These data can be used to replicate the analyses presented in the paper. If you wish to use the data for other purposes, please contact the authors for permission. Thank you.</p>
Towards the conservation of Brazilian legumes: Summary, land cover and use analytics, and a comparative analysis of locations count methods (curated vs. automated [buffer-dissolution])
<p>This dataset presents key findings from the study "<strong>Automating and Enhancing Species Extinction Risk Assessments with Historical Land Use and Land Cover Data</strong>."</p> <p>The file `<em>summary-threatened-legume-species.ods</em>` provides a summary of all threatened species of the Leguminosae family native to Brazil, including IUCN category and criteria, location counts, and the year of the latest assessment, sourced from official records. Additionally, it includes calculated values for Area of Occupancy (AOO) and Extent of Occurrence (EOO), along with trend data indicating natural area change rates (decline [positive number, red] or growth [negative number, green]) within both AOO and EOO. Location counts are further detailed across various buffer radii (1-5 km), utilizing a buffer-dissolution method for automated location counting. Each buffer radius is analyzed to assess AOO and EOO decline, where '1' indicates a species is threatened and '0' indicates it is not. This data provides an efficient method for screening threatened species under criterion B of the IUCN Red List guidelines.</p> <p>The file `<em>overlay-analysis.ods</em>` contains overlay analysis results for AOO and EOO of each species using MapBiomas land use and land cover (LULC) data (specifically MapBiomas Brazil, collection 7.1) from 1985 to 2021, covering all threatened legume species. This file provides both absolute area in square kilometers and percentages for each LULC class. The overlay analysis results support estimates of growth and decline trends for each LULC class.</p> <p>The file `<em>trend-analysis.ods</em>` presents results of annual rate estimates from trend analysis across LULC classes, and including both natural and anthropic groupings. A complete JSON database with p-values and R² values is provided in `<em>trend-analysis.json</em>`.</p> <p>This approach, combining all results for each species in a comprehensive, merged dataset, allows for effective filtering and ranking of the most threatened species as well as identification of their primary threats.</p> <p>We recommend opening the ODS files with LibreOffice, as Microsoft Excel may experience issues parsing decimal formats accurately.</p> <p>More detailed maps and graphs are available at <a title="LULC-MapBiomas-Leguminosae" href="https://github.com/lsbjordao/LULC-MapBiomas-Leguminosae" target="_blank" rel="noopener">https://github.com/lsbjordao/LULC-MapBiomas-Leguminosae</a>.</p>
Data from: Contrasting effects of land cover on nesting habitat use and reproductive output for bumble bees
<p><span><span><a name="_Hlk64216363">Understanding habitat quality is central to understanding the distributions of species on the landscape, as well as to conserving and restoring at-risk species. Although it is well-known that many species require different resources throughout their life cycles, pollinator conservation efforts focus almost exclusively on forage resources. In this study, we evaluate nesting habitat for bumble bees by locating nests directly on the landscape. We compared colony density and colony reproductive output for <i>Bombus impatiens, </i>the common eastern bumble bee, across three different land cover types (hay fields, meadows, and forests). We also assessed nesting habitat associations for all <i>Bombus</i> nests located during surveys to tease apart species-specific patterns of habitat use. We found that <i>B. impatiens</i> nested under the ground in two natural land cover types, forests and meadows, but found no <i>B. impatiens</i> nests in hay fields. Though <i>B. impatiens</i> nested at similar densities in both meadows and forests, colonies in forests had much higher reproductive output<i>. </i></a>In contrast, <i>B. griseocollis</i> tended to nest on the surface of the ground and was almost always found in meadows. <i>B. perplexis</i> was the only species to nest in all three habitat types, including hay fields. For some bumble bee species in this system, meadows, the habitat type with abundant forage resources, may be sufficient to maintain them throughout their life cycles. However, <i>B. impatiens</i> might benefit from heterogeneous landscapes with forests and meadows. Results for <i>B. impatiens</i> emphasize the longstanding notion that habitat use is not always positively correlated with habitat quality (as measured by reproductive output). Our results also show that habitat selection by bumble bees at one spatial scale may be influenced by resources at other scales. Finally, we demonstrate the feasibility of direct nest searches for understanding bumble bee distribution and ecology. </span></span></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.