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15 results for “river corridor”
Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.
<h2>Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.</h2><h4>Daniel Buscombe, Marda Science LLC</h4><p>There are two datasets in this data release:</p><p>1. <strong>Model training dataset</strong>. A manually (or semi-manually) labeled image dataset that was used to train and evaluate a machine (deep) learning model designed to identify subaerial accumulations of large wood, alluvial sediment, water, and vegetation in orthoimagery of alluvial river corridors in forested catchments. </p><p>2. <strong>Model output dataset</strong>. A labeled image dataset that uses the aforementioned model to estimate subaerial accumulations of large wood, alluvial sediment, water, and vegetation in a larger orthoimagery dataset of alluvial river corridors in forested catchments. </p><p>All of these label data are derived from raw gridded data that originate from the U.S. Geological Survey (<i>Ritchie et al., 2018</i>). That dataset consists of 14 orthoimages of the Middle Reach (MR, in between the former Aldwell and Mills reservoirs) and 14 corresponding Lower Reach (LR, downstream of the former Mills reservoir) of the Elwha River, Washington, collected between the period 2012-04-07 and 2017-09-22. That orthoimagery was generated using SfM photogrammetry (following <i>Over et al., 2021</i>) using a photographic camera mounted to an aircraft wing. The imagery capture channel change as it evolved under a ~20 Mt sediment pulse initiated by the removal of the two dams. The two reaches are the ~8 km long Middle Reach (MR) and the lower-gradient ~7 km long Lower Reach (LR). </p><p>The orthoimagery have been labeled (pixelwise, either manually or by an automated process) according to the following classes (inter class in the label data in parentheses):</p><p>1. vegetation / other (0)</p><p>2. water (1)</p><p>3. sediment (2)</p><p>4. large wood (3)</p><h3>1. Model training dataset.</h3><p>Imagery was labeled using a combination of the open-source software Doodler (<i>Buscombe et al., 2021</i>; <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler</a>) and hand-digitization using QGIS at 1:300 scale, rasterizeing the polygons, and gridded and clipped in the same way as all other gridded data. Doodler facilitates relatively labor-free dense multiclass labeling of natural imagery, enabling relatively rapid training dataset creation. The final training dataset consists of 4382 images and corresponding labels, each 1024 x 1024 pixels and representing just over 5% of the total data set. The training data are sampled approximately equally in time and in space among both reaches. All training and validation samples purposefully included all four label classes, to avoid model training and evaluation problems associated with class imbalance (<i>Buscombe and Goldstein, 2022</i>). </p><p>Data are provided in geoTIFF format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels.</p><p>Pixel-wise labels measurements such as these facilitate development and evaluation of image segmentation, image classification, object-based image-analysis (OBIA), and object-in-image detection models, and numerous potential other machine learning models for the general purposes of river corridor classification, description, enumeration, inventory, and process or state quantification. For example this dataset may serve in transfer learning contexts for application in different river or coastal environments or for different tasks or class ontologies.</p><h4>Files:</h4><p>1. Labels_used_for_model_training_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 63 MB, label tiffs</p><p>2. Model_<i>training_</i> images1of4.zip, 1.5 GB, imagery tiffs</p><p>3. Model_<i>training_</i> images2of4.zip, 1.5 GB, imagery tiffs</p><p>4. Model_<i>training_</i> images3of4.zip, 1.7 GB, imagery tiffs</p><p>5. Model_<i>training_</i> images4of4.zip, 1.6 GB, imagery tiffs</p><h3>2. Model output dataset.</h3><p>Imagery was labeled using a deep-learning based semantic segmentation model (<i>Buscombe, 2023</i>) trained specifically for the task using the Segmentation Gym (<i>Buscombe and Goldstein, 2022</i>) modeling suite. We use the software package Segmentation Gym (<i>Buscombe and Goldstein, 2022</i>) to fine-tune a Segformer (<i>Xie et al., 2021</i>) deep learning model for semantic image segmentation. We take the instance (i.e. model architecture and trained weights) of the model of <i>Xie et al. (2021)</i>, itself fine-tuned on ADE20k dataset (<i>Zhou et al., 2019</i>) at resolution 512x512 pixels, and fine-tune it on our 1024x1024 pixel training data consisting of 4-class label images.</p><p>The spatial extent of the imagery in the MR is [455157.2494695878122002,5316532.9804129302501678 : 457076.1244695878122002,5323771.7304129302501678] (NAD83(2011) / UTM zone 10N). Imagery width is 15351 pixels and imagery height is 57910 pixels. The spatial extent of the imagery in the LR is [457704.9227139975992031,5326631.3750646486878395 : 459241.6727139975992031,5333311.0000646486878395] (NAD83(2011) / UTM zone 10N). Imagery width is 12294 pixels and imagery height is 53437 pixels. Data are provided in Cloud-Optimzed geoTIFF (COG) format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels. All grids have been clipped to the union of extents of active channel margins during the period of interest.</p><p>Reach-wide pixel-wise measurements such as these facilitate comparison of wood and sediment storage at any scale or location. These data may be useful for studying the morphodynamics of wood-sediment interactions in other geomorphically complex channels, wood storage in channels, the role of wood in ecosystems and conservation or restoration efforts. </p><h4>Files:</h4><p>1. Elwha_MR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 9.67 MB, label COGs from Elwha River Middle Reach (MR)</p><p>2. Elwha<i>MR_ imagery_ part1_ of</i>_<i> </i>2.zip, 566 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>3. Elwha<i>MR_ imagery_ part2_ of</i>_<i> </i>2.zip, 618 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>3. Elwha_LR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 10.96 MB, label COGs from Elwha River Lower Reach (LR)</p><p>4. ElwhaL<i>R_ imagery_ part1_ of</i>_<i> </i>2.zip, 622 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>5. ElwhaL<i>R_ imagery_ part2_ of</i>_<i> </i>2.zip, 617 MB, imagery COGs from Elwha River Middle Reach (MR)<br> </p><p>This dataset was created using open-source tools of the Doodleverse, a software ecosystem for geoscientific image segmentation, by Daniel Buscombe (<a href="https://github.com/dbuscombe-usgs">https://github.com/dbuscombe-usgs</a>) and Evan Goldstein (<a href="https://github.com/ebgoldstein">https://github.com/ebgoldstein</a>). Thanks to the contributors of the Doodleverse!. Thanks especially Sharon Fitzpatrick (<a href="https://github.com/2320sharon">https://github.com/2320sharon</a>) and Jaycee Favela for contributing labels. </p><h3>References</h3><p>• Buscombe, D. (2023). <strong>Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery (v1.0)</strong> [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8172858">https://doi.org/10.5281/zenodo.8172858</a></p><p>• Buscombe, D., Goldstein, E. B., Sherwood, C. R., Bodine, C., Brown, J. A., Favela, J., et al. (2021).<strong> Human-in-the-loop segmentation of Earth surface imagery</strong>. Earth and Space Science, 9, e2021EA002085. <a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a></p><p>• Buscombe, D., & Goldstein, E. B. (2022). <strong>A reproducible and reusable pipeline for segmentation of geoscientific imagery.</strong> Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p><p>• Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, <strong>Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation</strong>: U.S. Geological Survey Open-File Report 2021–1039, 46 p., <a href="https://doi.org/10.3133/ofr20211039">https://doi.org/10.3133/ofr20211039</a>.</p><p>• Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, <strong>Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals</strong>: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/F7PG1QWC">https://doi.org/10.5066/F7PG1QWC</a>.</p><p>• Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P., 2021. <strong>SegFormer: Simple and efficient design for semantic segmentation with transformers</strong>. Advances in Neural Information Processing Systems, 34, pp.12077-12090.</p><p>• Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A. and Torralba, A., 2019. <strong>Semantic understanding of scenes through the ade20k dataset</strong>. International Journal of Computer Vision, 127, pp.302-321.</p><p><br> </p>
Wildlife along the Salt River corridor of the greater Phoenix, Arizona, USA metropolitan area: results of a camera-trapping project (2020-2021)
The goal of this research project was to evaluate how wildlife populations responded to the gradient of urbanization, water, and vegetation. We deployed 43 wildlife cameras across the gradient of urbanization January 2021 to January 2022. We documented a suite of wildlife species, from small mammals and birds to large mammals. Data present whether a species was detected at a site during this time period.
Data from: Interactions of wood accumulations, channel dynamics, and geomorphic heterogeneity within a river corridor
<p>Natural rivers are inherently dynamic. Spatial and temporal variations in water, sediment, and wood fluxes both cause and respond to an increase in geomorphic heterogeneity within the river corridor. We analyze 16 two-kilometer river corridor segments of the Swan River in Montana, USA to examine relationships between wood accumulations (wood accumulation distribution density, count, and persistence), channel dynamism (total sinuosity and average channel migration), and geomorphic heterogeneity (density, aggregation, interspersion, and evenness of patches in the river corridor). We hypothesize that i) more dynamic river segments correlate with a greater presence, persistence, and distribution of wood accumulations; ii) years with higher peak discharge correspond with greater channel dynamism and wood accumulations; and iii) all river corridor variables analyzed play a role in explaining river corridor spatial heterogeneity. Our results suggest that decadal-scale channel dynamism, as reflected in total sinuosity, corresponds to greater numbers of wood accumulations per surface area and greater persistence of these wood accumulations through time. Second, higher peak discharges correspond to greater values of wood distribution density, but not to greater channel dynamism. Third, persistent values of geomorphic heterogeneity, as reflected in the heterogeneity metrics of aggregation, interspersion, patch density, and evenness, are explained by potential predictor variables analyzed here. Our results reflect the complex interactions of water, sediment, and large wood in river corridors; the difficulties of interpreting causal relationships among these variables through time; and the importance of spatial and temporal analyses of past and present river processes to understand future river conditions</p>
Data from: Interactions of wood accumulations, channel dynamics, and geomorphic heterogeneity within a river corridor
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Spatial patterns and effects of invasive plants on soil microbial activity and diversity along river corridors - raw data
<p>Dataset for the study</p> <p><strong><span>Spatial patterns and effects of invasive plants on soil microbial activity and diversity along river corridors</span></strong></p> <ol> <li>environmental variables of the research plots</li> <li>vascular plant species composition of the research plots</li> <li>mcirobial activity on the research plots</li> <li>CLPP profiles of the research plots</li> </ol>
Data from: Drivers of geomorphic heterogeneity in unconfined non‐perennial river corridors
<p>River corridors along non-perennial stream networks are known to provide diverse physical and ecological functions that are thought to be related to geomorphic heterogeneity. Geomorphic heterogeneity refers to the spatial and temporal variability of geomorphic units, which are geomorphic patches within the river corridor formed by a given set of processes. While studies on the magnitude and drivers of geomorphic heterogeneity are emerging in perennial streams, similar studies in ephemeral streams are lacking. Given the ubiquity of non-perennial streams globally, we aim to answer questions regarding the magnitude and drivers of geomorphic heterogeneity in non-perennial river corridors as well as how geomorphic units and heterogeneity reflect processes related to flood disturbance. Geomorphic units were mapped in 30 unconfined river corridors within six non-perennial watersheds in Utah and Arizona, USA. Landscape heterogeneity metrics – Shannon's Diversity Index, Shannon's Evenness Index, and patch density – were used to quantify geomorphic heterogeneity within each reach. Additionally, variables that may potentially constrain or drive heterogeneity were quantified, including floodplain shape, grain size, large wood abundance, and proxies for flood disturbance. While heterogeneity positively correlated with metrics for morphology and disturbance, statistical models suggest that morphologic context, particularly floodplain width, was a more important predictor for estimating geomorphic heterogeneity. Still, geomorphic units reflected aggradation processes indicative of a range of flood energies, suggesting a strong tie between heterogeneity and disturbance. Results suggest that geomorphic heterogeneity may be resilient to changes in flood disturbance frequency or magnitude, but future studies investigating long-term temporal heterogeneity are needed.</p>
Data from: Drivers of geomorphic heterogeneity in unconfined non‐perennial river corridors
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Spatial patterns and effects of invasive plants on soil microbial activity and diversity along river corridors - dataset
<p>environmental data, plant community data, CLPP profiles, microbial activity data</p>
Distribution. CE Madagascar,in a forest patch of 228 km? at Andasivodihazo at about 1660 m above sea level and Ankadivory at about 1470 m above sea level; it may be restricted by the Onive and Mangoro rivers, Anjozorobe, and the corridor to the W half of the Tsinjoarivo forest above 1400 m, in an area ofless than 2000 km?, in Cheirogaleidae
Distribution. CE Madagascar,in a forest patch of 228 km? at Andasivodihazo at about 1660 m above sea level and Ankadivory at about 1470 m above sea level; it may be restricted by the Onive and Mangoro rivers, Anjozorobe, and the corridor to the W half of the Tsinjoarivo forest above 1400 m, in an area ofless than 2000 km?,
Distribution. CE & SE Madagascar; until recently restricted to the SC portion of the country's rainforests including Ranomafana and Andringitra national parks, Ambositra-Vondrozo Corridor and isolated forests between and to the E ofthese localities (e.g. Ambolomavo, Ifanadiana, Kianjavato), and one locality N of the Manampatrana River (Evendra); recent surveys have now confirmed its presence in the forests of Torotorofotsy, in the region of Andasibe-Mantadia, and 18 sites in and around the Ankeniheny-Zahamena Corridor, and at five additional sites around the Marolambo forest corridor and as far N as Zahamena National Park, and it extends to near the Midongy du Sud National Park in the S, 670 km S of Zahamena. Reports of large bamboo lemursfitting this species' description have been filtering in from various other remote sites (e.g. Mananara region). in Lemuridae
Distribution. CE & SE Madagascar; until recently restricted to the SC portion of the country's rainforests including Ranomafana and Andringitra national parks, Ambositra-Vondrozo Corridor and isolated forests between and to the E ofthese localities (e.g. Ambolomavo, Ifanadiana, Kianjavato), and one locality N of the Manampatrana River (Evendra); recent surveys have now confirmed its presence in the forests of Torotorofotsy, in the region of Andasibe-Mantadia, and 18 sites in and around the Ankeniheny-Zahamena Corridor, and at five additional sites around the Marolambo forest corridor and as far N as Zahamena National Park, and it extends to near the Midongy du Sud National Park in the S, 670 km S of Zahamena. Reports of large bamboo lemursfitting this species' description have been filtering in from various other remote sites (e.g. Mananara region).
Distribution. Restricted and patchy range in NE Madagascar that includes the humid forest belt extending from the Marojejy Massif and the Andapa Basin to Maroantsetra; the Androranga River may be the NE distributional limit in the Tsaratanana Corridor, although further surveys are needed to confirm this, and the Antainambalana River in the Makira Forest protected area is currently regarded as the S boundary. Currently, Marojejy represents the N limit ofits distribution, although historic range maps suggest that it once occurred as far N as the Bemarivo River near Sambava. The NE distributional limit in Makira was only recently established, when a few groups were found in the Antohaka Lava Forest, but informal reports suggest that the unprotected Maherivaratra Forest, outside NE Makira, may also contain Silky Sifakas. in Indriidae
Distribution. Restricted and patchy range in NE Madagascar that includes the humid forest belt extending from the Marojejy Massif and the Andapa Basin to Maroantsetra; the Androranga River may be the NE distributional limit in the Tsaratanana Corridor, although further surveys are needed to confirm this, and the Antainambalana River in the Makira Forest protected area is currently regarded as the S boundary. Currently, Marojejy represents the N limit ofits distribution, although historic range maps suggest that it once occurred as far N as the Bemarivo River near Sambava. The NE distributional limit in Makira was only recently established, when a few groups were found in the Antohaka Lava Forest, but informal reports suggest that the unprotected Maherivaratra Forest, outside NE Makira, may also contain Silky Sifakas.
Distribution. WC Laos, from S Bolikhamsal Province, probably with the Nam Kading River as the N barrier of the distribution, S to C Khammouane Province, at ¢.17° 40' N; there are also reports from the corridor to Nakai-Nam-Theun National Protected Area, and from Khamkeut District, close to Lak Sao (18° 13' N, 104° 47' E). in Cercopithecidae
Distribution. WC Laos, from S Bolikhamsal Province, probably with the Nam Kading River as the N barrier of the distribution, S to C Khammouane Province, at ¢.17° 40' N; there are also reports from the corridor to Nakai-Nam-Theun National Protected Area, and from Khamkeut District, close to Lak Sao (18° 13' N, 104° 47' E).
Data from: CO-RIP: a riparian vegetation and corridor extent dataset for Colorado River Basin streams and rivers
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Data from: Large wood decay state and piece shape in river corridors
Open the record for dataset details and reuse information.
Spatial patterns and effects of invasive plants on soil microbial activity and diversity along river corridors - raw data
<p>environmental data, plant community data, CLPP profiles, microbial activity data</p>
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