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331 results for “plantation”
Disentangling the impact of event- and annual-scale precipitation extremes on critical-zone hydrology in semiarid loess: A case study in apple tree plantation
<p>The dataset is the basic data of the author's paper ' Disentangling the impact of event-and annual-scale precipitation extremes on critical-zone hydrology in semiarid loess - a case study in apple tree plantation '. The main content of this paper is to study the hydrological effect of extreme precipitation on the critical area of semi-arid loess. Taking apple plantation as an example, the data set includes the soil moisture and soil temperature data monitored in the field and the apple tree transpiration data. The measured data are used to calibrate and verify the model used in this paper. The water vapor flux, apple tree evapotranspiration and soil leakage data of the simulated soil profile are also included to analyze the hydrological effect of extreme precipitation on the critical area of loess.</p>
Tree inventory data of P. menziesii var. menziesii (= viridis) (Schwerin) Franco in Pavari's Plot 412 ninty years after plantation.
<p>This dataset containing the historical series of dendrometric data of a Douglas fir (<em>P. menziesii var. menziesii (= viridis) (Schwerin) Franco)</em> plantation. Implemented in 1932, the plantation is located in place “Rio di Mercurella”, in the Tyrrhenian coastal mountain range in Calabria (Southern Italy). The experimental plot has been identified as Plot 412 and its Google Earth’s coordinates are 39°20'11.55"N e 16° 4'49.40"E. The forest stand is characterized by trees with a relevant role for forest community biodiversity.</p> <p>In 1940, the monitoring of this stand has been started. In this dataset, four of all inventories have been reported. Until 2013, these inventories have been carried out by Istituto Sperimentale di Selvicoltura and Unità di Ricerca per la Selvicoltura in Ambiente Mediterraneo, now incorporated in CREA Research centre of Forestry and Wood which has taken over the last two inventories.</p> <p>90 years after is plantations, in Plot 412 a total analysis has been performed. The following data have been collected: position, number, Diameter at breast height (DBH) of all trees, total height (Ht) of a sample of trees. All data have been elaborated to determine the mean dendrometric parameters as basal area (BA) and Volume (V). Volume of tree was calculated using the local volume table elaborated by Avolio in 1987 and it was integrated with measurements to model trees by Avolio e Bernardini in period 1988-1996.</p> <p>The data collected constitute a fundamental contribution to assess the health and stability of this forest stands. They represent an important historical source and evidence of first experimental test of the introduction of Douglas fir that must also be monitored in the future in Calabria.</p>
Impacts of pastures and forestry plantations on herpetofauna: a global meta-analysis
<p class="MsoListParagraphCxSpFirst"><span>1.</span><span> </span><span>The establishment of pastures and forestry plantations has increased globally to meet growing demands for meat and wood products. <a name="_Hlk108096120"></a>Pasture and plantation expansion often drives deforestation, which causes homogenization of biotic communities and is a major driver of the global extinction crisis. A core question is how the severity of losses varies between pastures and plantations, and in turn, how geographical location and plantation management characteristics moderate these impacts.</span></p> <p class="MsoListParagraphCxSpMiddle">2.<span> </span><span>Focusing on herpetofauna (amphibians and reptiles) as the most endangered vertebrate group, we performed a global synthesis using 41 scientific articles that reported species richness or abundance in pastures and forestry plantations relative to natural forest in 191 case studies among 19 countries. </span></p> <p class="MsoListParagraphCxSpMiddle">3.<span> </span><span>We found a severe negative effect of pasture and a less negative effect of forestry plantations on species richness and abundance of herpetofauna.</span> <span>Within plantations, species richness and total abundance were more negatively impacted in amphibians than reptiles, in the tropics, when planting exotic tree species, monocultures, large or commercial plantations, and when clearing understory vegetation. Yet mixed, old, small, or </span>conservation plantations and those permitting recovery of understory vegetation had no negative impacts relative to the reference natural forest.</p> <p class="MsoListParagraphCxSpLast">4.<span> </span><em><span>Synthesis and applications</span></em><span>. The loss of herpetofauna species richness and abundance underscores the importance of h</span>alting ongoing tropical deforestation for pasture and intensive forestry plantations. The potential for <span>a</span>ppropriately managed f<span>orestry plantations to support biodiversity in regions lacking forest cover, including via replacement of anthropogenic pastures, </span>suggests that such plantations have an important role <span>under global reforestation agendas.</span></p> <p> </p>
Spatio-temporal variation in deep soil water use patterns of overstory and understory layers in subtropical plantations predict community assembly
<p><span>1. </span><span>Deep soil water utilization allows plants to cope with drought stress. However, little is known about the roles of the understory layers in driving spatio-temporal variations of deep soil water in forests and how the patterns of deep soil water use among life forms contribute to community assembly processes.</span></p> <p><span>2. </span><span>We assessed the spatio-temporal patterns and determinants of deep water utilization of tree, shrub and herb layers in subtropical coniferous plantations and investigated associations between deep water use parameters and dominance and richness of understory vegetation. </span></p> <p><span>3. </span><span>We found that the understory layer had a higher reliance on deep soil water in the dry season, a larger seasonal plasticity of deep soil water uptake, but lower spatial variability in deep soil water utilization than the tree layer. We showed that greater reliance of the tree layer on deep soil water was associated with decreased shrub layer diversity, whereas greater reliance of the shrub layer on deep water was associated with increased herb layer diversity. </span></p> <p><span>4. </span><span>Synthesis</span><span>. Our results highlight the roles of understory layers in driving the temporal dynamics of deep soil water in forests and improve our understanding of how deep soil water use patterns amongst life forms shape community assembly in forests.</span></p>
Figure 3 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 3. Comparison of the number of interactions between frugivorous birds and plants between native forest and eucalyptus plantation in the PEIT. (a): Fecal samples interactions (P-value = 0.83, W = 3.5); (b): Focal observation interactions (P-value = 0.99, W = 4.0).
Figure 2 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 2. Interaction networks between frugivorous birds and zoochoric plants, according to the focal observations of birds in both sampled habitats. The circles represent the plant species, and the species of birds are represented by triangles. The acronyms in the center of the figures are the scientific names of the species (Supplementary material 1). The thickness of the links (lines) is related to the connectivity between each species (the thicker the line, the more records this interaction had). Each color represents a cluster of species that are more connected within each other than with between species from other clusters due to its modularity (Q). (a): Fragments of native forest; (b): Fragments of eucalyptus plantation.
Figure 1 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 1. Interaction networks between frugivorous birds and zoochoric plants, according to the fecal samples of birds in the understory of the two sampled habitats. The circles represent the plant species, and the triangles are representing the species of birds. The acronyms in the center of the figures are the scientific names of the species (Supplementary material 1). The thickness of the links (lines) is related to the connectivity between each species (the thicker the line, the more records this interaction had). Each color represents a cluster of species that are more connected within each other than with species from other clusters due to its modularity (Q). (a): F fragments of native forest; (b): Fragments of eucalyptus plantation.
Supplementary material 1 from: Baum S, Weih M, Bolte A (2012) Stand age characteristics and soil properties affect species composition of vascular plants in short rotation coppice plantations. BioRisk 7: 51-71. https://doi.org/10.3897/biorisk.7.2699
Number of plots containing the respective species is stated.
Dataset of site factors in Pinus sylvestris L. plantations in Spain
<p>This database contains information about soil, climatic, physiographic and stand parameters of 35 plots located in <em>Pinus sylvestris</em> L. plantations in Spain. </p>
Fine-scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data
<p>In recent years, LiDAR technology has gained widespread attention for its ability to provide precise 3D vertical structural data for various objects, particularly forests. In our dataset, we utilized LiDAR data to reconstruct intricately detailed three-dimensional representations of specific larch forest landscapes. These detailed forest structural models enable us to drive three-dimensional radiative transfer models, analyze the radiation budget of the forest canopy, and gain valuable insights into fine-scale forest management strategies.</p> <p>This is the research work we conducted by combining the aforementioned 3D forest scenes with the 3D RTM LESS. If you use our data, please cite our article. You can access our publication via DOI: 10.34133/plantphenomics.0166.</p> <p>We welcome researchers interested in a wide range of fields, such as vegetation ecological applications, to communicate with us by combining 3D vegetation modeling.</p> <p><br><br></p>
Output data for: Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia
<p>This repository contains the output data associated with the publication "Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia". It contains the FLAM modeled burned area and the GLOBIOM output, as well as the a downscaling grid.</p> <p>Descriptions of the results can be found in the publication (DOI will follow).</p>
Fig. 5. Hybrid 5 in E Va L U At I O N O F A L L E L I C C O N T E N T I N A N Experimental Alder (Alnus Spp.) Plantation
Fig. 5. Hybrid 5-year seedlings mean height (Hv) and A.glutinosa allele content.
Fig. 1 in Evaluation Of Winter Hardiness In Different Cultivated Tilia Taxa - Experience Of Some Most Valuable Dendrological Plantations In Central Latvia (Vidzeme) After Extremely Hard Winter In Year 2009/2010
Fig. 1. Location of inventoried dendrological objects in central part of Latvia.
Tea Plantation in Kericho County
<p>Tea is a major cash crop in Kenya</p>
The Tea plantation
<p>This is a photograph of our beautiful county</p>
Above-ground carbon density derived from LiDAR data over oil palm plantations in Malaysian Borneo, 2014
<b>Description: </b><p>The work was carried out in the oil palm plantations within the Stability of Altered Forest Ecosystem (SAFE) Project, located within lowland dipterocarp forest regions of East Sabah in Malaysian Borneo. Airborne LiDAR data were acquired on 5 November 2014 using a Leica LiDAR50-II flown at 1850 m altitude on a Dornier 228-201 travelling at 135 knots. The LiDAR sensor emitted pulses at 83.1 Hz with a field of view of 12.0°, and a footprint of about 40 cm diameter. The average pulse density was 7.3/m2. The Leica LiDAR50-II sensor records full waveform LiDAR, but for the purposes of this study the data were discretised, with up to four returns recorded per pulse. The LiDAR data were pre-processed by NERC's Data Analysis Node and delivered in standard LAS format. All further processing was undertaken using LAStools (Rapidlasso GmbH, LAStools). Points were classified as ground and non-ground, and a digital elevation model (DEM) was fitted to the ground returns, producing a raster of 1 m resolution. The DEM elevations were subtracted from elevations of all non-ground returns to produce a normalised point cloud, and a canopy height model (CHM) was constructed from this on a 0.5 m raster by averaging the first returns. Finally, holes in the raster were filled by averaging neighbouring cells. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/25"><b>Influences of disturbance and environmental variation on biomass change in Malaysian Borneo</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant, JKM/MBS.1000-2/2 JLD.3 (128))</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.3 (128))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247699">here</a></p><p><b>Files: </b>This consists of 1 file: LiDAR_Aboveground_Carbon.xlsx</p><p><b>LiDAR_Aboveground_Carbon.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>LiDAR aboveground carbon in Oil palm plantations</b> (described in worksheet LiDAR_ Aboveground_Carbon)</p><p>Description: The output of a LiDAR50-II sensor records full waveform LiDAR, but for the purposes of this study the data were discretised, with up to four returns recorded per pulse. The LiDAR data was pre-processed by NERC's Data Analysis Node and delivered in standard LAS format. All further processing was undertaken using LAStools (Rapidlasso GmbH, LAStools). Points were classified as ground and non-ground, and a digital elevation model (DEM) was fitted to the ground returns, producing a raster of 1 m resolution. The DEM elevations were subtracted from elevations of all non-ground returns to produce a normalised point cloud, and a canopy height model (CHM) was constructed from this on a 0.5 m raster by averaging the first returns. Finally, holes in the raster were filled by averaging neighbouring cells. </p><p>Number of fields: 35</p><p>Number of data rows: 27</p><p>Fields: </p><ul><li><b>Year</b>: Year the oil palm trees were planted (Field type: Numeric)</li><li><b>Plot</b>: Plot number based on the SAFE project framework. Each plot is 25 metres x 25 metres size or 0.0625 hectares (Field type: Location)</li><li><b>meanH</b>: Average tree height per plot (Field type: Numeric)</li><li><b>TreeN_plot</b>: Number of trees per plot (Field type: Numeric)</li><li><b>TreeN_ha</b>: Number of trees per hectare obtained by upscaling the number of trees within each 25m x 25m (0.0625 ha) plot to 1 ha (Field type: Numeric)</li><li><b>ACD_plot</b>: Sum of the aboveground carbon density per plot (Field type: Numeric)</li><li><b>ACD_ha</b>: Sum of the aboveground carbon density per hectare obtained by upscaling the number of aboveground carbon density within each 25m x 25m (0.0625 ha) plot to 1 ha (Field type: Numeric)</li><li><b>CC1</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 1 metre height (Field type: Numeric)</li><li><b>CC2</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 2 metres height (Field type: Numeric)</li><li><b>CC3</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 3 metres height (Field type: Numeric)</li><li><b>CC4</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 4 metres height (Field type: Numeric)</li><li><b>CC5</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 5 metres height (Field type: Numeric)</li><li><b>CC6</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 6 metres height (Field type: Numeric)</li><li><b>CC7</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 7 metres height (Field type: Numeric)</li><li><b>CC8</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 8 metres height (Field type: Numeric)</li><li><b>CC9</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 9 metres height (Field type: Numeric)</li><li><b>CC10</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 10 metres height (Field type: Numeric)</li><li><b>CC11</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 11 metres height (Field type: Numeric)</li><li><b>CC12</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 12 metres height (Field type: Numeric)</li><li><b>CC13</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 13 metres height (Field type: Numeric)</li><li><b>CC14</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 14 metres height (Field type: Numeric)</li><li><b>CC15</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 15 metres height (Field type: Numeric)</li><li><b>CC16</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 16 metres height (Field type: Numeric)</li><li><b>CC17</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 17 metres height (Field type: Numeric)</li><li><b>CC18</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 18 metres height (Field type: Numeric)</li><li><b>CC19</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 19 metres height (Field type: Numeric)</li><li><b>CC20</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 20 metres height (Field type: Numeric)</li><li><b>CC21</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 21 metres height (Field type: Numeric)</li><li><b>CC22</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 22 metres height (Field type: Numeric)</li><li><b>CC23</b>: Average canopy cover per hectare: the proportion of area occupied by crowns at 23 metres height (Field type: Numeric)</li><li><b>TCH</b>: Top of canopy height: mean height of Canopy Height Model (CHM) pixels per hectare. (Field type: Numeric)</li><li><b>TreeN_itc</b>: Number of segmented trees per hectare obtained by using the itcSegment function implemented in R (Field type: Numeric)</li><li><b>meanH_itc</b>: Average tree height per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li><li><b>meanHc_itc</b>: Corrected average tree height per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li><li><b>ACDc_itc</b>: Sum of the aboveground carbon density per hectare obtained by using the itcSegment function inmplement in R (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2014-11-05 to 2014-11-05</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Figure 1 in Neurozerra conferta (Lepidoptera: Cossidae) damaging Melaleuca plantations in Vietnam and its biological control
Figure 1. Distribution of Neurozerra conferta in Melaleuca plantations in Vietnam.
Figure 1 in The stem borer Zeuzera multistrigata Moore (Lepidoptera, Cossidae): a serious pest undermining Eucalyptus plantations in Northern Vietnam
Figure 1. Distribution of Zeuzera multistrigata in Eucalyptus plantations in Northern Vietnam.
Above and below ground biomass in three producing cranberry plantations in Latvia
<p>Results of analyses (biomass and carbon content) of samples collected in three cranberry plantations in Kaigu, Rāķu and Nidas mires. Above and below-ground biomass, leaves and berries separately. Carbon content in mixed sample.</p>
Mapped Industrial Tree Plantations (ITP) in Caraga Region, Mindanao, Philippines for the Year 2019
<p>The Esri Shapefile, in UTM 51 WGS 1984 coordinate reference system, contains polygons of industrial tree plantation species (Falcata, Bagras, Yemane, and Mangium) in the Caraga Region, Mindanao, Philippines. The plantations were mapped through the classification of year 2019 Sentinel-2 satellite images, complemented by high-resolution satellite images available in Google Earth, as well as field surveys conducted between October 1, 2019 to September 30, 2021.</p> <p>Please refer to the Project 1 terminal report (<a href="../records/13735736" target="_blank" rel="noopener">https://zenodo.org/records/13735736</a>) for more details.</p>
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