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Fig. 9 in XXII. Free-living Nematodes from Greenland, Land and Freshwater.
Fig. 9. Mononchus brachyuris. Tail.
Fig. 17 in XXII. Free-living Nematodes from Greenland, Land and Freshwater.
Fig. 17. Dorylaimus conicaudtus. Hind part of a specimen with more acute tail
India Onshore Wind Energy Atlas Accounting for Altitude and Land Use Restrictions and Co-Located Solar
<p>India faces the simultaneous challenges of meeting rising energy demand and reducing carbon emissions. To address these, India must transition to renewable energy sources. These high-resolution maps are used to quantify available areas for wind farms, after accounting for restrictions, including airports, buildings, protected land use, military zones, railways, roads, water bodies, waterways, wildlife and nature, high elevation and slope, and existing solar farms, to which policy-informed setback distances are applied. This study finds the wind and solar potential within available areas considering three altitudes (100 m, 150 m, 200 m) and four wind speed thresholds (5-8 m/s), and modern wind turbine and solar array dimensions. The raster files included here indicate available areas after aggregating restrictions for different combinations of altitude and wind speed threshold. Availability is indicated with a binary system in which available land is designated with a value of zero and restricted land is designated with a value of one.</p>
Alice in Quantum Land — Quantum Mechanics and its influence in (pop) culture
<p>In this seminar we will discuss Quantum Mechanics (QM) not so much from the traditional point of view of Physics, but to assess the inevitable “reverberations” that this discipline has had, and still has, in popular culture. Indeed, one of the aspects known even to the layman is the strangeness of the laws that govern the subatomic world, where totally counterintuitive things happen. Those who are used to doing science in the traditional way, resting their research on solid epistemological foundations, when confronted with QM must “suspend judgment” and very often use it – very pragmatically – as a simple tool (which Physicists have shown they can do very well).</p> <p>The seminar will focus on the bewilderment that the forerunners of this discipline already had to overcome to accept the “rules” it imposed, and we will see that among them even eminent minds made an enormous effort to accept these rules, when not even going so far as to reject this “new Physics” altogether.</p> <p>The seminar will then briefly explore the more “pop” side, which is how, from the point of view of popularizing science, QM offers excellent tools to intrigue a lay audience.</p>
Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use - Supplementary Materials
<p><strong>This repository contains supporting data for: "Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use"</strong></p> <ol> <li> <p><strong>Input Data</strong>: This file includes the model input data.</p> </li> <li> <p><strong>Payoff Matrix Solution</strong>: This file contains all decision variables of payoff matrix solutions (Cost optimal, Land-use optimal, and Water-use optimal solution).</p> </li> <li><strong>Compromise solution</strong>: This file contains all decision variables of the compromise solution.</li> <li> <p><strong>Pareto Curve</strong>: This file presents the Pareto curve solution. </p> </li> <li><strong>Pareto Curve Solution</strong>: This file contains all decision variables in each of the Pareto curve solutions. </li> </ol>
A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types
<p>A 10 m resolution land cover map of the Tibetan Plateau with 12 vegetation types and 3 non-vegetation types for the year 2022 (TP_LC10-2022) by leveraging state-of-the-art remote sensing approaches including the Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and Random Forest model using Google Earth Engine platform.</p>
Forest inventory, leaf area index, and leaf functional traits of various land cover classes in Kulen, Cambodia
<ol><li><strong>Sub-title 1: </strong>Forest inventory of evergreen forest, regrowth forest, and evergreen forest in Kulen, Cambodia. (<strong>File name: </strong><i>Forest_Inventory_Pub.txt)</i> <strong> </strong></li><li><strong>Sub-title 2: </strong>Species leaf area, chlorophyll a and b and leaf dry matter content of 30 species collected from evergreen forests, regrowth forests, and cashew plantation in Kulen, Cambodia. (<strong>File name:</strong> <i>Leaf_Trait_Species_Pub.txt)</i></li><li><strong>Sub-title 3: </strong>Canopy and total leaf area index from evergreen forests, regrowth forests, and cashew plantation in Kulen, Cambodia. (<strong>File name: </strong><i>LAI_Pub.txt </i>)</li></ol>
Database to: Effectiveness of soil management strategies for mitigation of N2O emissions in European arable land: A meta-analysis
<p>Database to a meta-analysis studing the effects of adding different organic matter inputs (crop residues, green manure, livestock manure, slurry, digestate, compost or biochar) to soils on N2O emissions. Database consists of over 50 field experiments conducted in 15 European countries. Diverse arable crops, mainly cereals, were cultivated in monoculture or in crop rotations on mineral soils. Cumulative N2O emissions per unit land area were monitored during periods of 30 to 1,070 days in treatments, which received organic matter inputs, alone or in combination with mineral N fertiliser; and in controls fertilised with mineral N. The original results appeared in 46 articles published between 1993 and 2022 in peer-reviewed scientific journals, as well as a project report, and a PhD thesis.</p>
Soil microbiome dataset from the University of Wisconsin Arlington and Lancaster agricultural research stations and cheese maker and vegetable processor wastewater land application sites
<p>Cheese making and vegetable processing are trillion-dollar industries globally. However, they generate immense volumes of high nitrogen wastewater that must be processed safely and cost effectively. Land application systems are frequently used by rural medium and smaller processing facilities that lack ready access to wastewater resource recovery facilities. This study utilized soil microbial data to determine system differences leading to high denitrification rates observed in incubation studies in agricultural soil collected from University of Wisconsin Agricultural Research Stations (ARS), Arlington and Lancaster stations, compared to industry cheese making and vegetable processing land application water treatment facilities. It was hypothesized that decade long frequent treatment with facility wastewater would alter the microbial communities in the system soils, but this is not the case. No clear correlations were found between soil denitrification rates and biotic or abiotic system factors and the microbial communities observed in the industry systems are similar to the ARS soils under agricultural production and to literature reported denitrifying systems such as wetlands and wastewater resource recovery facilities. Knowing that land application system management does not alter the microbial biome will allow any management advances that increase denitrification efficiency in other denitrifying systems to be readily applied to industry wastewater land application facilities. </p>
Exemplary validation of SMOS Level 3 version 339 Descending vs ERA5-Land v20190904 vs ISMN 20230110 global for EGU24
Exemplary QA4SM validation using FRMs for EGU24: SMOS Level 3 version 339 Descending vs ERA5-Land v20190904 vs ISMN 20230110 global. URL: https://qa4sm.eu/ui/validation-result/7f27889e-95e4-4e67-9a85-2e472cbb935c. Produced on QA4SM (https://qa4sm.eu)
A SSP1-Low emission land use scenario based on LCM2019 for Scotland - baseline 2019 and scenario 2050 (nov22)
<p>This set of datasets contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emission scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production.</p> <p>The baseline dataset is based on the Land Cover Map 2019 (Morton et al, 2020) aggregated at 100m resolution. Grazing intensity was added to it by using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). From the baseline dataset, the land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility that meets all the constraints stipulated for the scenario.</p> <p><strong>For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a></strong></p> <p>This analysis was conducted as part of the Land use Transformations (<a href="https://landusetransformations.hutton.ac.uk/">https://landusetransformations.hutton.ac.uk/</a>) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p> </p> <p><strong>-------------------------</strong></p> <p><strong>Datasets accessible here : <a href="https://openscience.hutton.ac.uk/dataset/low-emission-land-use-scenarios">SSP1-Low Emission Land Use Scenarios - Dataset - Natural Asset Register Data Portal (hutton.ac.uk)</a></strong></p> <p><strong>License</strong>: CC BY-NC 4.0 namely “Creative Commons Attribution-NonCommercial 4.0 International“ <a name="_Hlk161153952"></a>(https://creativecommons.org/licenses/by-nc/4.0/)</p> <p><strong>Copyright to display of the datasets</strong>: <br>“Contains Data owned by UK Centre for Ecology & Hydrology © Database Right/Copyright UKCEH. Based on Data from LPIS and JAC (Scottish Government, 2019).”</p> <p><strong>2 Main files :</strong></p> <ul> <li><strong>SSP1LEonLCM19_2019.tif </strong>: original land uses (2019) on which the scenario is based on. This land use map, of a resolution of 100m, is based on the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021).<br><br><strong>Contributions </strong>to the baseline dataset (SSP1LEonLCM19_2019.tif) : <ul> <li>100% of 100m cells: Land Cover Map 2019 (Morton et al, 2020)</li> <li>66.84% of 100m cells: the LCM 2019 was subdivided by grazing intensity using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This impacts the grasslands, heathers, bogs and arable classes.</li> <li>Estimated overall contributions: 90% UKCEH, 10% JHI</li> </ul> </li> </ul> <ul> <li><strong>SSP1LEonLCM19_2050.tif</strong> : land use scenario (2050), which is within the scope of a SSP1 - Low emissions scenario (Shared Scocio-Economic Pathways). The scenario was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a><br><br><strong>Contributions</strong> to the scenario dataset (SSP1LEonLCM19_2050.tif) : <ul> <li>cf. contribution to the baseline (above)</li> <li>14% of 100m cells: modelled land use change</li> <li>Estimated overall contributions: 85% UKCEH, 15% JHI</li> </ul> </li> </ul> <p> </p> <p><strong>Main references:</strong></p> <p>Morton, R. D., Marston, C. G., O’Neil, A. W., & Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB) [Data set]. NERC Environmental Information Data Centre. <a href="https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC">https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC</a></p> <p>Wardell-Johnson, D. (2022) Stocking rates derived from IACS 2019 version 4. <br>Based on data from Land Parcel Information System (2019) courtesy of Rural Payments and Inspections Division, Scottish Government.<br>Based on data from the June Agricultural Census (2019) courtesy of Rural and Environment Science and Analytical Services, Agricultural Statistics team, Scottish Government.</p> <p>Chapman, P. (2007) Conservation Grazing of Semi-natural Habitats. Technical note TN586. SAC tn586-conservation.pdf (sruc.ac.uk)</p> <p>FAS (2021) Practical Guide: Managing Peatlands and Upland Habitats. <a href="https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/">https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/ </a>(author: Paul Chapman)</p> <p>Castellazzi, M.S.; Gimona, A. (2021) SLM-OptionsTool, a land use change tool for Ecosystem Services (arcgis toolbox and user manual included, part of RESAS Deliverable-O1.4.2ciiD27).</p> <p>Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown I., Conrad, K.F., Perry J.N. (2010). Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale . Environmental Modelling and Software 25, 1881-1889. <a href="https://doi.org/10.1016/j.envsoft.2010.04.006">https://doi.org/10.1016/j.envsoft.2010.04.006</a> </p> <p><a href="https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts">https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts</a></p>
Data from: Land-use intensity and relatedness to native plants promote exotic plant invasion in a tropical biodiversity hotspot
<p>Exotic plant invasions threaten biodiversity and are costly to farmers. Land use is a major pathway promoting the spread of exotic plant species; however, little is known about the processes underlying the success of exotic plants in tropical agricultural landscapes. Focussing on the heterogeneous smallholder landscapes of north-eastern Madagascar, we studied exotic plants of understorey communities across a land-use intensity gradient from unburned lands (old-growth forests, forest fragments, and forest-derived vanilla agroforests) to burned ones (fallow-derived vanilla agroforests, woody fallows, and herbaceous fallows). </p> <p>We quantified the absolute species richness, abundance, and cover of exotic plants across land-use types and their proportional contribution to community richness, abundance, and cover as indicators of exotic plant invasion. We tested for the effects of land-use parameters, namely land-use history, canopy closure, and landscape-level forest cover, on exotic plants. Additionally, we tested whether the phylogenetic relatedness between exotic and native species in the same plot affected invasion success, testing Darwin's naturalization and pre-adaptation hypotheses. </p> <p>All indicators of exotic plant invasion were lowest in old-growth forests and forest fragments and highest in fallow-derived vanilla agroforests, woody fallows, and herbaceous fallows. Absolute and proportional exotic richness was negatively affected by canopy closure, and landscapes with high forest cover had lower proportions of exotic plant richness. High phylogenetic relatedness between exotics and natives was associated with lower proportional richness but higher proportions of exotics in abundance and cover. However, individual exotic species showed contrasting responses to land-use parameters and relatedness to natives.</p> <p>Synthesis and applications: Our results indicate that maintaining unburned lands, land-use types with dense canopies, and landscapes with high forest cover prevents the spread of exotic plants within agricultural landscapes of north-eastern Madagascar. Supporting Darwin's pre-adaptation hypothesis, exotic plants phylogenetically closely related to native plants are more likely to become successful invaders in terms of abundance and cover. Nevertheless, individual species show different responses to land-use changes and phylogenetic relatedness. Therefore, land-use decisions and management choices can be tailored to limit the spread of exotic species and to preserve native plants in this global biodiversity hotspot.</p>
Land Cover Change Hotspots from 2001 to 2021
<p><span>Land cover change hotsposts based on the harmonized global land cover maps from the ESA Climate Change Initiative, WorldCover, and Copernicus Global Land Cover Service from 2001 to 2021 at 0.1x0.1 degree grid</span></p>
Data from: Land consolidation impacts the abundance and richness of natural enemies but not pests in small-holder rice systems
<p>Traditional small-holder agricultural landscapes in southern China are being consolidated to increase mechanization levels in agriculture, but it is unclear how this influences rice arthropod communities in these landscapes. Here, based on a six-year study in 20 rice fields, we evaluated the impact of land consolidation on arthropod communities, crop damage, and rice yield. We also analyzed how effects of land consolidation were moderated by the proportion of large semi-natural habitat patches and insecticide use. We found that, compared to consolidated fields, rice fields in traditional farmlands had a higher abundance and family richness of natural enemies, but a similar abundance of rice pests. Land consolidation did not significantly interact with the proportion of large semi-natural habitat patches or insecticide application, in terms of affecting arthropods. The proportion of semi-natural habitat reduced the negative effect of insecticide application on key rice pests, but no equivalent interaction occurred for natural enemies.</p> <p><em>Syntheses and Applications:</em> Land consolidation can have negative impacts on the abundance and richness of natural enemies, but not pests in small-holder rice systems, and these impacts are independent from insecticide application and proportion of semi-natural habitat in the landscape. We recommend the implementation of agri-environmental measures or re-establishing field margin vegetation during the consolidation process to mitigate these potential negative effects, although trade-off between enhancing crop yields and preserving rice arthropod biodiversity should be considered. We encourage future research to focus on the detailed assessment of the function of linear habitats for a better understanding of the impact of land consolidation.</p>
HexaLCSeg: Hexagon-based Historical Land Cover Benchmark Dataset
<p>This dataset is a research outcome of a European Research Council, Proof of Concept Grant funded (Grant Number 101100837, A GeoAI-based Land Use Land Cover Segmentation Process to Analyse and Predict Rural Depopulation, Agricultural Land Abandonment, and Deforestation in Bulgaria and Turkey, 1940-2040, <a href="https://cordis.europa.eu/project/id/101100837">GeoAI_LULC_Seg</a>) project.</p> <p>We introduce a new benchmark dataset derived from very high-resolution historical Hexagon (KH-9) reconnaissance satellite images for use in deep learning-based image segmentation tasks. Our dataset comprises high-resolution monochromatic Hexagon images from the 1970s and 1980s covering Turkish and Bulgarian territories, encompassing a large geographic area.</p> <p>Land cover (LC) classes used in this study:<br>Our dataset is inspired by the European Space Agency (ESA) WorldCover project and includes eight LC classes and related RGB codes were set for each class but we adjusted the 0-pixel value as no data and replaced the 0 values with 1 in the ESA RGB code palette. Additionally, a new sub-class for the trees, named Permanent Cropland is defined and its RGB code was set to 1-207-117. This class is important to differentiate permanent fruit trees from other trees, specifically crucial for past agricultural mapping purposes.</p> <p>The HexaLCSeg dataset comprises eight panchromatic images accompanied by corresponding 3-channel RGB Ground Truth Masks, all with 8-bit radiometric resolution and a spatial resolution of 1 meter. The dataset is organized into a total of 10,000 patches, each sized at 256x256 pixels. We split our dataset into 70% training (7000 patches), 15% validation (1500 patches), and 15% testing (1500 patches).</p> <p>Methodology:<br>In our study, we employed the geographic object-based image analysis (GEOBIA) approach to generate accurate land cover (LC) maps, which serve as the ground truth masks for our dataset.</p> <p>For deep learning-based image segmentation, we employed a total of 9 CNN models, implementing U-Net++ and DeepLabv3+ segmentation architectures with different hyperparameters, paired with SE-ResNeXt50 backbone that pre-trained with weight values from the 2012 ILSVRC ImageNet dataset.</p> <p>Models, metric results and weights:</p> <table> <tbody> <tr> <th>Model No</th> <th>Architecture</th> <th>Loss Function</th> <th>Augmentation</th> <th>Loss</th> <th>Accuracy</th> <th>IoU</th> <th>F-1 Score</th> <th>Precision</th> <th>Recall</th> </tr> </tbody> <tbody> <tr> <td>Model 1</td> <td>U-Net++</td> <td>Focal Loss</td> <td>No Augmentation</td> <td>0.1252</td> <td>0.9734</td> <td>0.8052</td> <td>0.8804</td> <td>0.8805</td> <td>0.8803</td> </tr> <tr> <td>Model 2</td> <td>U-Net++</td> <td>Focal Loss</td> <td>Horizontal Flip</td> <td>0.1253</td> <td>0.9728</td> <td>0.8008</td> <td>0.8776</td> <td>0.8778</td> <td>0.8774</td> </tr> <tr> <td>Model 3</td> <td>DeepLabv3+</td> <td>Focal Loss</td> <td>No Augmentation</td> <td>0.1255</td> <td>0.9720</td> <td>0.7959</td> <td>0.8739</td> <td>0.8744</td> <td>0.8734</td> </tr> <tr> <td>Model 4</td> <td>U-Net++</td> <td>Focal Loss</td> <td>Random BC</td> <td>0.1256</td> <td>0.9717</td> <td>0.7938</td> <td>0.8725</td> <td>0.8727</td> <td>0.8723</td> </tr> <tr> <td>Model 5</td> <td>DeepLabv3+</td> <td>Dice Loss</td> <td>Horizontal Flip</td> <td>0.1292</td> <td>0.9714</td> <td>0.7928</td> <td>0.8714</td> <td>0.8717</td> <td>0.8711</td> </tr> <tr> <td>Model 6</td> <td>DeepLabv3+</td> <td>Dice Loss</td> <td>No Augmentation</td> <td>0.1307</td> <td>0.9711</td> <td>0.7906</td> <td>0.8699</td> <td>0.8702</td> <td>0.8697</td> </tr> <tr> <td>Model 7</td> <td>DeepLabv3+</td> <td>Focal Loss</td> <td>Horizontal Flip</td> <td>0.1257</td> <td>0.9711</td> <td>0.7897</td> <td>0.8698</td> <td>0.8704</td> <td>0.8692</td> </tr> <tr> <td>Model 8</td> <td>DeepLabv3+</td> <td>Focal Loss</td> <td>Random BC</td> <td>0.1259</td> <td>0.9704</td> <td>0.7871</td> <td>0.8667</td> <td>0.8673</td> <td>0.8662</td> </tr> <tr> <td>Model 9</td> <td>DeepLabv3+</td> <td>Dice Loss</td> <td>Random BC</td> <td>0.1401</td> <td>0.9691</td> <td>0.7793</td> <td>0.8608</td> <td>0.8612</td> <td>0.8604</td> </tr> </tbody> </table> <p> </p> <p>System-specific notes and configuration:</p> <p>The code was implemented in Python (3.10) Programming Language.</p> <p>- torch == 2.1.2<br>- segmentation-models-pytorch == 0.3.3<br>- Albumentations == 1.4.0</p> <p>Apart from main data science libraries, RS-specific libraries such as GDAL, rasterio, and tifffile are also required.</p> <p>Citation:<br>Please kindly cite our paper if this code and the dataset used in the study are useful for your research.</p> <p>Elif Sertel et al., “HexaLCSeg: A Historical Benchmark Dataset from Hexagon Satellite Images for Land Cover Segmentation [Software and Data Sets],” <em>IEEE Geoscience and Remote Sensing Magazine</em> 12, no. 3 (September 2024): 197–206, <a href="https://doi.org/10.1109/MGRS.2024.3394248">https://doi.org/10.1109/MGRS.2024.3394248</a>.</p>
FIG. 1 in Demographic and spatial structure at the stage of expansion in the populations of some alien land snails in Belgorod city (Central Russian Upland)
FIG. 1. Map of study sites. РИС. 1. Карта раЗмеЩения исследуемых участков.
10-meter Fine Land Cover product (FLC10) for China in circa 2022
<p>We have developed a 10-meter Fine Land Cover product (FLC10) for China, circa 2022. This product employs the detailed United Nations Land Cover Classification System (UNLCCS), which comprises 16 land-cover types. The manuscript is under review.</p>
Restoration of vegetation and soil in the Horqin Sandy Land, Northern China
<p>These data aimed to clarify the response of vegetation and soil properties to different restoration measures and dune topography, and to quantify dynamics over time. Vegetation and soil characteristics were evaluated at different topographic positions on dunes in the Horqin Sandy Land, with 5-, 25- and 35-year monitoring periods for grazing exclusion and tree planting, and 5- and 25-year monitoring periods for shrub planting.</p>
Data for: Carbon benefits through fallow agricultural land transitions: the case of multi-strata agroforestry in Hawaiʻi
<p>Bremer, L.L. (1,2), McGuire, G. (3,4), DeMaagd, N. (1,5), Trauernicht, C. (5)</p> <p><strong> </strong></p> <p>1 University of Hawaiʻi Economic Research Organization, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>2 Water Resources Research Center, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>3 Department of Geography and Environment, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p>4 Institute of Pacific Islands Forestry, USDA Forest Service, Hilo, HI, 96720</p> <p>5 Department of Natural Resources and Environmental Management, University of Hawaiʻi at Mānoa, Honolulu, HI, 96822</p> <p> </p> <p><strong>Bremer, L.L., McGuire, G., Hastings Silao, Z., Kurashima, N., Ticktin, T., Crow, S.E., Giardina, P.C., Winter, K.B., DeMaagd, N., and C. Trauernicht. Carbon benefits through fallow agricultural land transitions: the case of multi-strata agroforestry in Hawaiʻi</strong></p> <p> </p> <p>Multi-strata agroforestry land use scenarios were created using data from the: 2020 State of Hawaiʻi Agricultural Baseline (Perroy and Collier 2020), the Hawaiʻi Carbon Assessment (Jacobi et al. 2017), current (Giambelluca et al. 2013) and RCP 8.5 mid-century projected (Ellison-Timm et al. 2015) rainfall rasters, state land use zoning (SLUC, 2020), slope, elevation, and historical colluvial agroforestry maps (Kurashima et al. 2019). The following rasters display multi-strata agroforestry land-use scenarios. </p> <ul> <li> <p>“MS_currentclimate_landuse_scenario” represents potential multi-strata agroforestry under current rainfall.</p> </li> <li> <p>“MS_RCP85_midcentury_landuse_scenario” represents potential multi-strata agroforestry under projected RCP 8.5 mid-century rainfall. </p> </li> </ul> <p>For both scenarios: 1 = dry (550-1500 mm) multistrata agroforestry; 2 = mesic (1500-3000 mm) multi-strata agroforestry; 3 = wet (>3000 mm) multi-strata agroforestry.</p> <p>Estimates of projected changes in above-ground carbon were estimated by comparing modeled AGC in agroforestry scenarios to baseline AGC for current forest (Asner et al. 2016) and estimates of AGC in non-forest vegetation (Selmants et al. 2017). The following rasters display projected directional changes in soil carbon with agroforestry under current and RCP 8.5 mid century rainfall.</p> <ul> <li> <p>“AGC_change_currentclimate”</p> </li> <li> <p>“AGC_change_RCP85_midcentury”</p> </li> </ul> <p>For both scenarios: -2 = significant decrease (projected maximum < baseline); -1 = trend decrease (projected maximum > baseline > projected mean; 1 = weak increase (projected minimum < baseline < projected mean); 2 = strong increase (projected minimum > baseline).</p> <p>Estimates of projected changes in soil carbon under each scenario were estimated using global meta-analyses (Cardinael et al. 2018; Chaterjee et al. 2018; De Stefano and Jacobson 2017), studies of multi-strata agroforestry transitions in similar climates and soil types, and land-use change studies in Hawaiʻi. The following rasters display projected directional changes in soil carbon with agroforestry under current and RCP 8.5 mid-century rainfall. </p> <ul> <li> <p>“SoilC_currentclimate”</p> </li> <li> <p>“SoilC_RCP85_midcentury”</p> </li> </ul> <p>For both scenarios, 1 = increase low confidence; 10 = increase medium confidence; 100 = increase high confidence; 2 = no change low confidence; 20 = no change medium confidence; 200 = no change high confidence; 3 =unclear (insufficient data); 4 = unclear (mixed evidence).</p> <p>Projected synergies and tradeoffs in AGC and soil C under each scenario were estimated by combining the soil C and AGC results. The following rasters display projected synergies and tradeoffs in soil C and AGC under current and RCP 8.5 mid-century rainfall. </p> <ul> <li> <p>“AGC_Soil_Bivariate_currentclimate”</p> </li> <li> <p>“AGC_Soil_Bivariate_RCP85_midcentury”</p> </li> </ul> <p>For both scenarios: the first value is soil C category: 7 = unknown/uncertain; 8= increase; 9= no change; and the second value is AGC category: 0 = decrease; 1 = no change; 2 = increase.</p> <p><strong> </strong></p> <h2>References:</h2> <p>Asner, G. P., Sousan, S., Knapp, D. E., Selmants, P. C., Martin, R. E., Hughes, R. F., & Giardina, C. P. (2016). Rapid forest carbon assessments of oceanic islands: A case study of the Hawaiian archipelago. 11(1). <a href="https://doi.org/10.1186/s13021-015-0043-4">https://doi.org/10.1186/s13021-015-0043-4</a></p> <p>Cardinael, R., Umulisa, V., Toudert, A., Olivier, A., Bockel, L., & Bernoux, M. (2018). Revisiting IPCC Tier 1 coefficients for soil organic and biomass carbon storage in agroforestry systems. Environmental Research Letters, 13. <a href="https://doi.org/10.1088/1748-9326/aaeb5f/meta">https://doi.org/10.1088/1748-9326/aaeb5f/meta</a></p> <p>Chaterjee, N., Nair, P. K. R., Chakraborty, S., & Nair, V. D. (2018). Changes in soil carbon stocks across the forest-agrofoest-agriculture/pasture continuum in various agroecological regions: A meta-analysis. Agriculture, Ecosystems & Environment, 266, 55–67. <a href="https://doi.org/10.1016/j.agee.2018.07.014">https://doi.org/10.1016/j.agee.2018.07.014</a></p> <p>De Stefano, A., & Jacobson, M. G. (2017). soil carbon sequestration in agroforestry systems: A meta-analysis. Agroforestry Systems. <a href="https://doi.org/10.1007/s10457-017-0147-9">https://doi.org/10.1007/s10457-017-0147-9</a></p> <p>Elison Timm, O., Giambelluca, T. W., & Diaz, H. F. (2015). Statistical downscaling of rainfall changes in Hawaiʻi based on the CMIP5 global model projections. Journal of Geophysical Research: Atmospheres. <a href="https://doi.org/10.1002/2014JD22059">https://doi.org/10.1002/2014JD22059</a></p> <p>Giambelluca, T. W., Chen, Q., Frazier, A. G., Price, J. P., Chen, Y. L., Chu, P. S., Eischeid, J. K., & Delparte, D. M. (2013). Online Rainfall Atlas of Hawaiʻi. Bulletin Of the American Meteorological Society, 94, 313–316. <a href="https://doi.org/10.1175/BAMS-D-11-00228.1">https://doi.org/10.1175/BAMS-D-11-00228.1</a></p> <p>Jacobi, J. D., Price, J. P., Fortini, L. B., Gon III, S. M., & Berkowitz, P. (2017). Carbon Assessment of Hawaiʻi Land Cover Map [Map]. USGS. </p> <p><a href="https://www.sciencebase.gov/catalog/item/592dee56e4b092b266efeb6b">https://www.sciencebase.gov/catalog/item/592dee56e4b092b266efeb6b</a></p> <p>Kurashima, N., Fortini, L., & Ticktin, T. (2019). The potential of indigenous agricultural food production under climate change in Hawaiʻi. Nature Sustainability. <a href="https://doi.org/10.1038/s41892-019-0226-1">https://doi.org/10.1038/s41892-019-0226-1</a></p> <p>Selmants, P. C., Giardina, C. P., Sousan, S., Knapp, D. E., Kimball, H., Hawbaker, T. J., Moreno, A., Seirer, J., Running, S. W., Miura, T., Bergstrom, R., Hughes, R. F., Litton, C. M., & Asner, G. P. (2017). Baseline Carbon Storage and Carbon Fluxes in Terrestrial Ecosystems of Hawaiʻi. USGS.</p> <p>State Land Use Commission. (2020). State Land Use District Boundaries [Map]. Hawaiʻi Statewide GIS Program.</p>
GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change
<p>We complied the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change (GSOCS-LULCC) from 632 papers documented in Web of Science till the June 2024. This database comprises 1,187 sites with 5,805 records at multiple sample depths.<br>This dataset (in csv formats) is associated to the "GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change" by Chen et al. (2025). The README file includes the full explanation of all the columns.<br>Manuscript citation: Chen, S., Shuai, Q., Arrouays, D., Chen, Z., Dai, L., Hong, Y., Hu, B., Huang, Y., Ji, W., Li, S., Liang, Z., Ma, Y., Richer-de-Forges, A.C., Schillaci, C., Su, Y., Teng, H., Wang, N., Wang, X., Wang, Y., Wang, Z., Wang, Z., Xu, D., Xue, J., Ye, S., Zhang, X., Zhou, Y., Zhu, P., Shi, Z. , 2025. GSOCS-LULCC: the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change. In preparation.<br>When using the data, please cite repositories as well as the original manuscript.<br>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
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