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135 results for “forest land”
SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Carpinteria US Forest Service Office (CarpinteriaUSFS383)
Precipitation was collected by the Santa Barbara County Flood Control District at Carpinteria US Forest Service Office (CarpinteriaUSFS383) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc
Global agricultural land use scenarios for estimating the potential of forest regeneration for climate mitigation to 2050
<p>The dataset includes 90 global food system and land use scenarios developed with the model BioBaM-GHG 2.0. The scenarios have been developed for assessing the global potential of forest regeneration for climate mitigation to 2050 under various food system pathways, i.e. diets, crop yield developments, land requirements for energy crops, and two variants of grassland use.</p> <p>The scenarios include the following data on country level: Land use and land-use change, cropland area by crop group, grazing area by quality classes, crop production by crop groups, crop consumption by crop groups and use types, crop wastes (losses), net imports/exports, production and consumption of animal products, grass supply and demand, GHG emissions from land-use change, GHG emissions from agricultural activities, and total cumulated GHG emissions.</p> <p>The main model result in this context, cumulative carbon sequestration from forest regeneration until 2050, is calculated as difference between the parameters "GHG emissions from land use change (cumulative) (Mt CO2e)" and "GHG emissions from land use change excluding C stock changes from natural succession (cumulative) (Mt CO2e)".</p> <p>Please refer to the related publication "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0" (Kalt et al., 2021 - currently under review at Ecological Modelling) for further information.</p> <p>This work was funded by the Austrian Science Fund (FWF) within project P29130-G27 GELUC.</p>
CESM2 land output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2015-2100. The files are labelled according to the experiments they were generated from (base, MF (Max Forest) and No LULCC). Output fields are as follows:</p> <p>discharge_plus_runoff: surface water availability (river discharge plus surface runoff), units m^-3 s^-1</p> <p>EFLX_LH_TOT: total latent heat flux from land to atmosphere, units W m^-2</p> <p>QFLX_EVAP_TOT: total evapotranspiration (canopy evaporation plus canopy transpiration plus soil evaporation), units kg m^-2 s^-1</p> <p>SOILLIQ: soil liquid water content, units kg m^-2</p> <p>SW_surface_albedo: surface albedo, units fraction</p> <p>TSA: 2m air temperature, units K</p> <p>VEGWP: vegetation water potential, units m</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 land use data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land use/ land cover input data for CESM2 which was used in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2000-2100. The files correspond to experiments described in the study as follows:</p> <p> </p> <p>Base: landuse.timeseries_0.9x1.25_SSP1-2.6_78pfts_CMIP6_simyr2000-2100_c220715.nc</p> <p>Max Forest: landuse.timeseries_0.9x1.25_hist_78pfts_SSPRFAFRS_SSP1_edit_xarray_4_simyr2000-2100_c221024.nc</p> <p>No LULCC: landuse.timeseries_0.9x1.25_hist_78pfts_SSPNOLULCC_3_simyr2000-2100_c221025.nc</p> <p> </p> <p>Files were created in collaboration by James A. King, James Weber, Peter Lawrence, and Stephanie Roe.</p> <p> </p>
Rapid ant community re-assembly in a Neotropical forest: recovery dynamics and land-use legacy
<p>Regrowing secondary forests dominate tropical regions today, and a mechanistic understanding of their recovery dynamics provides important insights for conservation. In particular, land-use legacy effects on the fauna have rarely been investigated. One of the most ecologically dominant and functionally important animal groups in tropical forests are ants. Here, we investigated the recovery of ant communities in a forest – agricultural habitat mosaic in the Ecuadorian Chocó region. We used a replicated chronosequence of previously used cacao plantations and pastures with 1 – 34 years of regeneration time to study the recovery dynamics of species communities and functional diversity across the two land use legacies. We compared two independent components of responses on these community properties: resistance, which is measured as the proportion of an initial property that remains following the disturbance; and resilience, which is the rate of recovery relative to its loss. We found that compositional and trait structure similarity to old-growth forest communities increased with regeneration age, whereas ant species richness remained always at a high level along the chronosequence. Land-use legacies influenced species composition, with former cacao plantations showing higher resemblance to old-growth forests than former pastures along the chronosequence. While resistance was low for species composition and high for species richness and traits, all community properties had similarly high resilience. In essence, our results show that ant communities of the Chocó recovery rapidly, with former cacao reaching predicted old-growth forest community levels after 21 years and pastures after 29 years. Recovery in this community was faster than reported from other ecosystems and was likely facilitated by the low-intensity farming in agricultural sites and their proximity to old-growth forest remnants. Our study indicates the great recovery potential for this otherwise highly threatened biodiversity hotspot.</p>
Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series - Data and Material
<p>This dataset contains the Enhanced Vegetation Index (EVI) data used in our research work about land surface phenology of Andean Araucaria-Nothofagus forests as well as the phenology information derived from it.</p> <p>Study area: Conguillío National Park, Chile<br> Study period: 2016-2020</p> <p>Description of datasets:</p> <p>conguillio.sen2.lnd8.evi.2016.2020.nc - A raster dataset (NetCDF) of EVI values (resolution 10m). EVI was calculated from Level-2 Sentinel-2 and Landsat 8 data. To ensure harmonization, the Landsat 8 data was resampled and reprojected to Sentinel-2 properties prior to the index calculation.</p> <p>evi_gb_beck_white.tif - A raster dataset (GeoTiff) of phenological metrics per year (resolution 10m). Metrics were derived by fitting a double logistic function (see Beck et al., 2006) to the smoothed and interpolated EVI pixel time series. Subsequently, the main phenological variables SOS (start of season) and EOS (end of season) were extracted using a 50% threshold value. The dataset itself is a result of the R package "greenbrown" and the layers are named accordingly (see https://greenbrown.r-forge.r-project.org/phenology.php). It is available as GeoTIFF and as R rasterfile.</p> <p>Details about the methodology and results describing this dataset can be found in the following publication:<br> Kosczor, E., Forkel, M., Hernández, J., Kinalczyk, D., Pirotti, F. & Kutchartt, E., 2022. Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series. Int. J. Appl. Earth Obs. Geoinf. 112, 102862. https://doi.org/10.1016/j.jag.2022.102862</p>
Realizing COP26's declaration on deforestation protects forests at the expense of non-forest land
<p>Data and model source code for the manuscript: "Realizing COP26's declaration on deforestation protects forests at the expense of non-forest land"</p> <p>Abhijeet Mishra1,2,*, Florian Humpenöder1, Christopher P.O. Reyer1, Felicitas Beier1,2, Hermann Lotze-Campen1,2, and Alexander Popp1</p> <p>1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany</p> <p>Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> September 2022</p> <p>See https://github.com/abhimishr/magpie/releases and https://github.com/magpiemodel/magpie/releases/tag/v4.5.0 for further details</p>
Land Use and Land Cover Mapping of Katanino Forest Reserve, Zambia (2019–2023)
<h1><strong>Overview</strong></h1> <p>The land use and land cover maps encompass the Katanino Forest Reserve in the Copperbelt province, Zambia. These maps categorize the area into two classes: forest and non-forest. They were derived from NICFI, Sentinel-2, and Sentinel-1 mosaics, resulting in a spatial resolution of 4.77 meters, covering the period from 2019 to 2023. </p> <h1><strong>Maps Accuracy</strong></h1> <p>The overall accuracy of the final annual maps (2019–2023) ranged from 0.90 to 0.94. The user’s and producer’s accuracies are detailed in Table 1.</p> <p>Table 1: Land use and land cover maps validation, including overall, producer (PA) and user (UA) accuracies values for each class.</p> <table> <tbody> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2019</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2020</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2021</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2022</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>2023</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> </td> </tr> <tr> <td> <p><strong><span> </span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> <td> <p><strong><span>PA</span></strong></p> </td> <td> <p><strong><span>UA</span></strong></p> </td> </tr> <tr> <td> <p><span>Forest</span></p> </td> <td> <p><span>0.87</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.83</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.91</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.88</span></p> </td> <td> <p><span>1</span></p> </td> </tr> <tr> <td> <p><span>Non Forest</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.86</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.82</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.99</span></p> </td> <td> <p><span>0.89</span></p> </td> <td> <p><span>1</span></p> </td> <td> <p><span>0.87</span></p> </td> </tr> <tr> <td> <p><strong><span>Overall Accuracy</span></strong></p> </td> <td> <p><strong><span>0.92</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.90</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.93</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.94</span></strong></p> </td> <td> <p><span> </span></p> </td> <td> <p><strong><span>0.93</span></strong></p> </td> <td> <p><span> </span></p> </td> </tr> </tbody> </table> <h1><strong>Files descripion</strong></h1> <ul> <li>KAT_2019.tif: 2019 land use and land cover map</li> <li>KAT_2020.tif: 2020 land use and land cover map</li> <li>KAT_2021.tif: 2021 land use and land cover map</li> <li>KAT_2022.tif: 2022 land use and land cover map</li> <li>KAT_2023.tif: 2023 land use and land cover map</li> <li>qgis_style.qml: QGIS style file</li> <li>KAT_training_samples(.shp, .shx, .dbf, .prj): training samples with class labels</li> <li>KAT_validation_samples(.shp, .shx, .dbf, .prj): validation samples with class labels</li> </ul> <p> </p>
The unpatterned orange morph of Philippine Boiga cynodon photographed in 2016 in the University of the Philipines at Los Baños Quezon Land Grant Forest Reserve, Municipality of Siniloan, Quezon Province, southeastern Luzon Island, Photo: Rafe M. Brown. in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
The unpatterned orange morph of Philippine Boiga cynodon photographed in 2016 in the University of the Philipines at Los Baños Quezon Land Grant Forest Reserve, Municipality of Siniloan, Quezon Province, southeastern Luzon Island, Photo: Rafe M. Brown.
Figure 3 in Ant (Hymenoptera: Formicidae) species diversity in secondary forest and three agricultural land uses of the Colombian Pacific Coast
Figure 3. Number of shared ant species and total number of specimens caught (pitfall and Winkler sack) between four areas of different land use. Two oil palm plots of three and seven years of age were pooled. El Mira Research Center, Tumaco, Pacific Coast of Colombia. / Número de especies de hormigas compartidas y número total de individuos capturados (Pitfall y sacos Winkler) entre cuatro áreas con diferente uso de tierra. Las dos parcelas de palma de aceite de tres y siete años fueron agrupadas. Centro de Investigación El Mira, Tumaco, Nariño, costa pacÍfica de Colombia.
Figure 1 in Ant (Hymenoptera: Formicidae) species diversity in secondary forest and three agricultural land uses of the Colombian Pacific Coast
Figure 1. Map of El Mira Research Center of the Corporación Colombiana de Investigación Agropecuaria, Tumaco, Nariño, Pacific Coast of Colombia, with the location (arrows) of the pitfall trap transects. Yellow hybrid oil palm 7 years old; red hybrid oil palm 3 years old; black peach palm; white secondary forest. / Mapa del Centro de Investigación El Mira de la Corporación Colombiana de Investigación Agropecuaria, Tumaco, Nariño, costa pacÍfica de Colombia con la ubicación (flechas) de las trampas pitfall en los transectos. Amarillo palma de aceite hÍbrido 7 años; rojo palma de aceite hÍbrido 3 años; negro palma de chontaduro; blanco bosque secundario.
Figure 2 in Ant (Hymenoptera: Formicidae) species diversity in secondary forest and three agricultural land uses of the Colombian Pacific Coast
Figure 2. Variation in 0D diversity (species number) of Formicidae between four areas of different land use: El Mira Research Center, Tumaco, Pacific Coast of Colombia. SF: secondary forest, PP: Peach palm, OP7: Oil palm 7 years old, OP3: Oil palm 3 years old. / Variación en la diversidad 0D (número de especies) de Formicidae entre cuatro áreas con diferente uso de tierra. Centro de Investigación El Mira de la Corporación Colombiana de Investigación Agropecuaria, Tumaco, Nariño, costa pacÍfica de Colombia.
Fig. 1 in Successional patterns of carabid fauna (Coleoptera: Carabidae) in planted and natural regenerated pine forests growing on old arable land
Fig. 1: Principal components analysis (PCA) carried out with the dataset. Years of study are given in brackets behind the study areas/sites.
Linked collectors and determiners for: Solanum hydroides (Solanaceae): a prickly novelty from the land of the sugar loaves, central Brazilian Atlantic Forest.
Natural history specimen data linked to collectors and determiners held within, "Solanum hydroides (Solanaceae): a prickly novelty from the land of the sugar loaves, central Brazilian Atlantic Forest". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7b405761-b22d-48bd-9d78-afe3e77e47a5">https://bionomia.net/dataset/7b405761-b22d-48bd-9d78-afe3e77e47a5</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7b405761-b22d-48bd-9d78-afe3e77e47a5">https://gbif.org/dataset/7b405761-b22d-48bd-9d78-afe3e77e47a5</a>. Formatted as a Frictionless Data package.
A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC)
<p>A boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from black spruce (Picea mariana)-dominated mature forest stands.</p> <p>Dataset glossary and users’ instructions are documented in ‘README.md’. </p>
APPENDIX 12 in Detangling the effects of patch attributes on bryophyte diversity in fragmented subtropical secondary forests - a case study of land-bridge islands
APPENDIX 12. — Relationships of accumulative species number with accumulative sampling efforts for eight largest islands.
FIG. 2 in Detangling the effects of patch attributes on bryophyte diversity in fragmented subtropical secondary forests - a case study of land-bridge islands
FIG. 2. — Relationships of species richness with number of habitat types, area, elevation, shape irregularity, vegetative cover, and ISW for five bryophyte categories in 168 forest fragments of the Thousand Island Lake, China. The regression equations are derived from GLMMs. Note: ISW, the relative proportion of water within a circle of a diameter of 1000 m centered on a given island.
FIGURE 2 in Trophic niche size and overlap in temperate forest land snails are affected by their lifestyle and body size
FIGURE 2 Variation in SEAc of land snail species from the four studied assemblages in relation to their lifestyle (a) and body size (b). Variation in percentages of overlapping SEAc for pairwise species combinations are compared between study sites (c) and three types of lifestyle (d). Different letters refer to significant differences at p<0.0, tested by GEE (a, b) and GLM-qp (c, d). The central line of each box refers to the median value, box height to the interquartile range, whiskers to the non-outlier range (i.e., 1.5 times the interquartile range at each side), and small circles to outliers.
FIGURE 1 in Trophic niche size and overlap in temperate forest land snails are affected by their lifestyle and body size
FIGURE 1 Isotopic niches represented by Standard ellipse area corrected for small sample size (SEAc) of the land snail species collected in four study sites (A, B, C, D) at least in five individuals per site.
Documenting twenty years of the contracted labor-intensive forestry workforce on National Forest System lands in the United States
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