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Dataset results
113 results for “Forestry”
Supplementary material 4 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Plant pathogenic protist assessment for Zambia
Supplementary material 2 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Guidelines for scoring species
Supplementary material 5 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Assessment for vector species
Supplementary material 1 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
All data from horizon scanning for Zambia
Data from: Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning
<p><strong>Paper Abstract:</strong></p> <p>Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R<sup>2</sup>: 0.90, RMSE: 10.1%), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R<sup>2</sup>: 0.67, RMSE: 2.73%), and in residential areas, capturing the totality of canopy cover variation (R<sup>2</sup>: 0.85, RMSE: 5.66%). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R<sup>2</sup>: 0.86, RMSE: 11.4%), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34% canopy cover). The two cities (Brampton and Mississauga) have 15.9% and 17.5% canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes.</p> <p> </p> <p><strong>Data details:</strong></p> <p>See paper. </p>
Supplementary material 1 from: Zhao X, Zhang J, Guo H (2021) Development evaluation of nature reserves under China's forestry department: A spatiotemporal empirical study at the province level. Nature Conservation 44: 81-97. https://doi.org/10.3897/natureconservation.44.65488
Tables S1, S2
Data from: Potential of typical highland and mountain forests in the Czech Republic for climate-smart forestry: ecosystem-scale drought responses
<p>Climate-smart forestry (CSF) consists of an extensive framework of actions directed to mitigating and adapting to global climate change impacts on the resilience and productivity of forest ecosystems. The study connected to this data set investigates the impact of the pan-European 2018 drought on carbon exchange dynamics in typical highland and mountain forests in the Czech Republic, including two coniferous (Norway spruce at Bílý Kříž and Rajec) and one deciduous (European beech at Štítná) stand. Our results show annual net ecosystem CO<sub>2</sub> uptake at Rajec to be reduced by 50% during the drought year in comparison to a reference year with normal climatic conditions. Bílý Kříž is less affected by drought, as the local microclimate ensures sufficient water supply. The European beech forest at Štítná is most resilient against drought and its negative impacts: there we detect no differences in carbon exchange dynamics between the drought year and the reference year. Our results are demonstrated on the basis of monthly and annual carbon exchange values and corresponding environmental variables. This data set consists of two files, one containing daily average (sum) data, the second one containing 30 minute average data. The 30 minute average data were the basis of all daily, monthly and annual average (sum) data shown in the study connected to this data set.</p>
Analyzing environmental and social impacts of urban forestry practices in Tacoma, WA with PlanIT Geo
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Data from: Potential of typical highland and mountain forests in the Czech Republic for climate-smart forestry: ecosystem-scale drought responses
Open the record for dataset details and reuse information.
Data from: Effects of forestry-driven changes to groundcover and soil moisture on amphibian desiccation, dispersal, and survival
Over 80% of amphibian species that are declining are forest dependent. Forestry practices are a major cause of forest alterations globally, and it is well documented that clearcutting can contribute to amphibian declines. However, there might be adverse effects of forestry practices other than clearcutting. For example, planting overstory trees in rows (plantations) can change groundcover microhabitats and soil moisture levels, but the effects of this common practice on amphibian populations are not well studied. We compared the impacts of common intensive pine plantation operations to naturally regenerated pine forests on the desiccation, movement rates, behavior, and survival of > 900 juvenile Southern toads (Anaxyrus terrestris). Pine plantations had significantly more accumulation of conifer needles and less exposed soil, herbaceous groundcover, broadleaf litter, and soil moisture than natural pine forests despite the greater canopy cover at plantations. Litter cover explained 85% of groundcover microhabitat variance among forest types and predicted minimum soil moisture levels. When toads were held in small outdoor enclosures that constrained microhabitat selection, 24-h desiccation rates and 72-h mortality were significantly greater in pine plantation than in naturally regenerated pine forest because of lower soil moisture, especially during low rainfall periods. In large outdoor pens where juvenile amphibians could select microhabitats, movement was strongly directed down slope and increased with precipitation. However, initial speeds were positively associated with pine density, likely because toads were trying to evacuate from the drier high-pine-density areas. High-intensity silviculture practices that eliminate herbaceous or vegetative groundcover, such as roller chopping and scalping, increase amphibian desiccation because planted conifers dry the upper soil layer. Our study highlights the importance of prioritizing lower intensity silviculture practices or lower pine densities to retain groundcover microhabitat that serves as amphibian refugia from dry conditions that are predicted to increase in frequency with climate change.
Data from: Effects of forestry-driven changes to groundcover and soil moisture on amphibian desiccation, dispersal, and survival
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ForClearingDet - Image dataset of annotated RGB images for object detection in forestry clearing operations
<p>Image dataset of 4 annotated forestry object classes (tree trunks, rocks, vegetation and humans) for object detection during forestry clearing operations. The imbalance of the object class "human" in this dataset was compensated for by the utilisation of the dataset available at <a href="https://www.kaggle.com/datasets/karthika95/pedestrian-detection">https://www.kaggle.com/datasets/karthika95/pedestrian-detection</a>.</p>
Offroad UGV environment sensing : a multimodal dataset for farming and forestry operations
<p>The aim of this dataset is to provide a multimodal dataset for environmental perception tasks in agricultural and forestry environments, in the case of autonomous land vehicle navigation. This evolving dataset will be updated regularly throughout 2023.</p> <p><br> This dataset is provided by Technology & Strategy Engineering SAS and is jointly funded by the Agence Nationale de la Recherche et de la Technologique (ANRT). This work was carried out in collaboration with the University of Haute Alsace, and more specifically the MIAM-FOTI team of the IRIMAS EA7499 laboratory.</p>
ScienceDex guides
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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.