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62 results for “area covered”
Data from: Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula
1. Antarctica is an iconic region for scientific explorations as it is remote and a critical component of the global climate system. Recent climate change causes dramatic retreat of ice in Antarctica with associated impacts to its coastal ecosystem. These anthropogenic impacts have a potential to increase habitat availability for Antarctic intertidal assemblages. Assessing the extent and ecological consequences of these changes requires us to develop accurate biotic baselines and quantitative predictive tools. 2. In this study, we demonstrated that satellite based remote sensing, when used jointly with in-situ ground-truthing and machine learning algorithms, provides a powerful tool to predict the cover and richness of intertidal macroalgae. 3. The salient finding was that the Sentinel-based remote sensing described a significant proportion of variability in the cover and richness of Antarctic macroalgae. The highest performing models were for macroalgal richness and the cover of brown and green algae as opposed to the model of red algal cover. 4. When expanding the geographical range of the ground-truthing, even involving only a few sample points, it becomes possible to potentially map other Antarctic intertidal macroalgal habitats and monitor their dynamics. This is a significant milestone as logistical constraints are an integral part of the Antarctic expeditions. The method has also a potential in other remote coastal areas where extensive in-situ mapping is not feasible.
Air temperature measurements using autonomous self-recording dataloggers in mountainous and snow covered areas
<p>Data and sctripts employed on submitted article on Water Resources Research (AGU Journal)</p>
Luhansk region tree cover change area estimation (1996-2020)
<p><strong>Occupation of Environment: Long-Term Consequences of war in Ukraine on conservation of forested areas of Emerald Network in the Luhansk region - </strong>excel sheets with stratified random sampling area estimation for Luhansk region based on the ground reference data. The division into territories controlled by Ukraine and Russia made based on the demarcation line with consideration of the buffer zone, where Ukrainian institutions were disconnected due to the active warfare in purpose of separation of zones with interruption of Ukrainian environmental policies.</p>
Riparian land-cover data and model code for: Multiple-region, N-mixture community models to assess associations of riparian area, fragmentation, and species richness
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Data from: Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula
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Relationships between a common Caribbean corallivorous snail and protected area status, coral cover, and predator abundance
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Land cover classification of the central Arizona-Phoenix area using Landsat (MSS) data - year 1973
These data represent a land use classification of the central Arizona-Phoenix area. They were created using a Landsat MSS image for the year 1973.
Land use and land cover (LULC) classification of the CAP LTER study area using 2010 Landsat imagery
The land use and land cover (LULC) mapping generated from the 30 meter resolution Landsat TM5 is prepared for CAP LTER analyses. The series of products includes three levels LULC classifications, from coarser land-cover types to finer, hybrid LULC types, arranged in three thematic maps with contrasting numbers of LULC categories: (1) 12, (2) 15, and (3) 21. The percent of vegetation cover (vegetation fraction) in the residential area is provided. The image has a resampled spatial resolution of 15 meters due to the image classification procedure.
Land cover classification of the Central Arizona-Phoenix area using Landsat Thematic Mapper (TM) data - year 1993
Land cover classification for the Central Arizona-Phoenix CAP LTER study region using Landsat Thematic Mapper (TM) data - for the year 1993
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
<p>Mastering the evolution of urban land cover is important for urban management and planning. In this paper, a method for analyzing land cover evolution within urban built-up areas based on nighttime light data and Landsat data is proposed. The method solves the problem of inaccurate descriptions of urban built-up area boundaries from the use of single-source diurnal or nocturnal remote sensing data and was able to achieve an effective analysis of land cover evolution within built-up areas. Four main procedures are involved: (1) The neighborhood e<span>xtremum</span> method and maximum likelihood method are used to extract nighttime light data and the urban built-up area boundaries from the Landsat data, respectively; (2) multisource urban boundaries are obtained using boundary pixel fusion of the nighttime light data and Landsat urban built-up area boundaries; (3) the maximum likelihood method is used to classify Landsat data within multisource urban boundaries into land cover classes, such as impervious surface, vegetation and water, and to calculate landscape indexes, such as overall landscape trends, degree of fragmentation and degree of aggregation; (4) the changes in the multisource urban boundaries and landscape indexes were obtained using the abovementioned methods, which were supported by multitemporal nighttime light data and Landsat data, to model the urban land cover evolution. Using the cities of Shenyang, Changchun and Harbin in northeastern China as experimental areas, the multitemporal landscape index showed that the integration and aggregation of land cover in the urban areas had an increasing trend, the natural environment of Shenyang and Harbin was improving, while Changchun laid more emphasis on the construction of artificial facilities. At the same time, the method proposed in this paper to extract built-up areas from multi-source city data showed that the user accuracy, production accuracy, overall accuracy and Kappa coefficient are at least 3%, 1%, 1% and 0.04 higher than the single-source data method.</p>
Landscape cover type, not social dominance, is associated with the winter movement patterns of snowy owls in temperate areas
<p>Migrating animals occur along a continuum from species that spend the nonbreeding season at a fixed location to species that are nomadic during the nonbreeding season, essentially continuously moving. Such variation is likely driven by the economics of territoriality or heterogeneity in the environment. The Snowy Owl (<i>Bubo scandiacus</i>) is known for its complex seasonal movements, and thus an excellent model to test these ideas, as many individuals travel unpredictably along irregular routes during both the breeding and nonbreeding seasons. Two possible explanations for this large variation in the propensity to move are: (1) dominance hierarchies in which dominant individuals (adult females in this case) monopolize some key, consistent resources, and move less than subdominants and (2) habitat heterogeneity in which individuals foraging in rich and less heterogenic environments are less mobile. We analyzed fine-scale telemetry data (GPS/GSM) from 50 Snowy Owls tagged in eastern and central North America from 2013–2019, comparing space use during the winter period according to sex and age, and to land cover attributes. We used variograms to classify individuals as nomadic (58%) or range-resident (42%), and found that nomadic owls had ten times larger wintering areas than range-resident owls. The frequency of nomadism was similar in socially-dominant adult females, immatures and males. However, nomadism increased from west to east, and north to south, and was positively associated with use of water and negatively associated with croplands. We conclude that many individual Snowy Owls in Eastern North America are nomadic during the nonbreeding season and that movement patterns during this time are driven primarily by extrinsic factors, specifically heterogeneity in habitat and prey availability, as opposed to intrinsic factors associated with spacing behavior, such as age and sex.</p>
Text–fig. 1. Map of the Anti-Atlas area (Morocco) with the sampled locality in the Zagora region (marked with a black star) (after Gutiérrez-Marco et al. 2003, and Sumrall and Zamora 2011). Key: a, Precambrian and Palaeozoic rocks; b, Ordovician rocks; c, post-Palaeozoic cover. in Pauxillites Thaddei A New Lower Ordovician Hyolith From Morocco
Text–fig. 1. Map of the Anti-Atlas area (Morocco) with the sampled locality in the Zagora region (marked with a black star) (after Gutiérrez-Marco et al. 2003, and Sumrall and Zamora 2011). Key: a, Precambrian and Palaeozoic rocks; b, Ordovician rocks; c, post-Palaeozoic cover.
Landscape cover type, not social dominance, is associated with the winter movement patterns of snowy owls in temperate areas
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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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CLPX-Satellite: EO-1 Hyperion Surface Reflectance, Snow-Covered Area, and Grain Size, Version 1
This data set consists of apparent surface reflectance, subpixel snow-covered area, and grain size collected from the Hyperion hyperspectral imager. The Hyperion imager has a spectral range of 400-2500 nm, a spectral resolution of 10 nm, spatial resolution of 30 m, and a swath width of 7.8 km. Sampling is scene based (256 samples, 512 lines).
BOREAS Follow-On DSP-09 Moss Cover Classification at Three Area Scales
BOREAS follow-on group DSP-9 mapped surface moss type at three scales (1 km, 30 m, and 10 m) based on observed associations between moss cover and land cover type. In the BOREAS Northern (NSA) and Southern (SSA) Study Areas, we utilized land cover derived from Landsat TM (30 m) and ground measurements/observations, soils maps, and field observations to establish associations between moss and land cover. At the BOREAS regional scale, the 1 km moss cover map was developed using a 1 km AVHRR land cover map for a 619 by 821 km subset of the BOREAS region. Our regional moss cover map is largely based on inferences from the 1 km land cover analysis and from ground observations in the study areas. The 30 m moss map covers the BOREAS Southern Study Area. The 10 m map covers the BOREAS NSA Old Black Spruce tower site.
PROVE Land Cover and Leaf Area of Jornada Experimental Range, New Mexico, 1997
Field measurement of shrubland ecological properties is important for both site monitoring and validation of remote-sensing information. During the NASA Earth Observing System Prototype Validation Exercise (PROVE) at the Jornada Experimental Range, New Mexico, on May 20-30, 1997, we calculated plot-level plant area index, leaf area index, total fractional cover, and green fractional cover with data from four instruments: (1) a Dycam Agricultural Digital Camera (ADC), (2) a LI-COR LAI-2000 plant canopy analyzer, (3) a Decagon sunfleck ceptometer, and (4) a laser altimeter. Estimates from the LAI-2000 and ceptometer were very similar (plant area index 0.3, leaf area index 0.22, total fractional cover 0.19, green fractional cover 0.14), but the ADC produced values 5 to 10% higher. Laser altimeter values, depending on the height cutoff used to establish total fractional cover, were either higher or lower than the other instruments' values: a 10-cm cutoff produced values approximately 80% higher, whereas a 20-cm cutoff produced values approximately 30% lower.
LBA-ECO LC-03 SAR Images, Land Cover, and Biomass, Four Areas across Brazilian Amazon
This data set provides three related land cover products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include (1) orthorectified JERS-1 and RadarSat images, (2) land cover classifications derived from the SAR data, and (3) biomass estimates in tons per hectare based on the land cover classification. There are 12 image files (.tif) with this data set.Orthorectified JERS-1 and RadarSat images are provided as GeoTIFF images - one file for each study area.For the Manaus and Tapajos sites: The images are orthorectified at 12.5-meter resolution and then re-sampled at 25-meter resolution.For the Rondonia and Rio Branco sites: The images from 1978 are orthorectified at 25-meter resolution and then re-sampled at 90-meter resolution. Each GeoTIFF file contains 3 image channels: - 2 L-band JERS-1 data in Fall and Spring seasons and - 1 C-band RadarSat data.Land cover classifications are based on two JERS-1 images and one RadarSat image and provided as GeoTIFFs - one file for each study area. Four major land cover classes are distinguished: (1) Flat surface; (2) Regrowth area; (3) Short vegetation; and (4) Tall vegetation. The biomass estimates in tons per hectare are based on the land cover classification results and are reported in one GeoTIFF file for each study area.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products.KNOWN PROBLEMS: The data providers note that due to limited resources, these data have been neither validated nor quality-assured for general use. For that reason, extreme caution is advised when considering the use of these data.Any use of the derived data is not recommended because the results have not been validated. However, the DEM and vectors (related data set), and orthorectified SAR data can be used if the user understands how these were produced and accepts the limitations.
Figure 1 in Effect of land cover on biodiversity and composition of a soil macrofauna community in a reclaimed coastal area at Yancheng, China
Figure 1. The distribution of sample sites on the reclaimed coast.
Glacier Covered Area for the State of Alaska, 1985-2020, Version 1
This data set captures changes in glacier covered area across the state of Alaska for the period 1985 to 2020.The data set includes 18 biannual shapefiles each for overall glacier covered area, supraglacial debris area, and debris-free glacier covered area.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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