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7 results for “above-ground carbon”
Factors to predict above-ground biomass carbon carrying capacity
<p>The climate data (Mean annual temperature (°C, MAT), mean annual precipitation (mm, MAP), annually accumulated temperature with days ≥ 0°C (°C-days, AAT0), annually accumulated temperature with days ≥ 10°C (°C-days, AAT10), aridity index, and humidity index ), soil properties (soil texture and soil types) and DEM are available from the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (https://www.resdc.cn/); The geological elements and hydrological elements data can be found at http://dcc.ngac.org.cn/geologicalData/rest/geologicalData/geologicalDataDetail/402881f75d9bc077015d9bc084160000and https://www.webmap.cn/commres.do?method=result25W; The geomorphology data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/zh-hans/data/63e290d7-7087-462a-acac-50195fba530b/). All data were resampled at 500m resolution.</p>
The percentage of above-ground biomass carbon carrying capacity reached
<p>This dataset is the percentage of above-ground biomass carbon carrying capacity reached in the eight provinces of southern China from 2002 to 2017 at the resolution of 500m x 500m, with the urban and water areas, cropland, and the southeast margin of the Tibet Plateau masked. The dataset takes values ranging from 0%-100%. 0% represents the highest carbon sequestration potential, while 100% represents carbon sequestration has reached saturation. The dataset can locate areas where vegetation has not yet reached its full potential, which is significant for the implementation and adjustment of ecological engineering. The dataset is publicly available.</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>
Water availability drives above-ground biomass and bird richness in forest restoration plantings to achieve carbon and biodiversity co-benefits
<p>To combat global warming and biodiversity loss we require effective forest restoration that encourages recovery of species diversity and ecosystem function to deliver essential ecosystem services, such as biomass accumulation. Further, understanding how and where to undertake restoration to achieve carbon sequestration and biodiversity conservation would provide an opportunity to finance ecosystem restoration under carbon markets. We surveyed 30 native mixed-species plantings in subtropical forests and woodlands in Australia, and used structural equation modelling to determine vegetation, soil and climate variables most likely driving above-ground biomass accrual and bird richness, and investigate the relationships between plant diversity, above-ground biomass accrual and bird diversity. We focussed on woodland and forest-dependent birds, and functional groups at risk of decline (insectivorous, understorey-nesting, and small-bodied birds). We found that mean moisture availability strongly limits above-ground biomass accrual and bird richness in restoration plantings, indicating potential synergies in choosing sites for carbon and biodiversity purposes. Counter to theory, woody plant richness was a poor direct predictor of above-ground biomass accrual, but was indirectly related via significant, positive effects of stand density. We also found no direct relationship between above-ground biomass accrual and bird richness, likely because of the strong effects of moisture availability on both variables. Instead, moisture availability and patch size strongly and positively influenced the richness of woodland and forest dependent birds. For understorey-nesting birds, however, shrub cover and patch size predicted richness. Stand age or area of native vegetation surrounding the patch did not influence bird richness. Our results suggest that in subtropical biomes, planting larger patches to higher densities, ideally using a diversity of trees and shrubs (characteristics of ecological plantings) in more mesic locations will enhance the provision of carbon and biodiversity co-benefits. Further, ecological plantings will aid the rapid recovery of woodland and forest bird richness, with comparable above-ground biomass accrual to less diverse forestry plantations.</p>
Data from: Spatial structure of above-ground biomass limits accuracy of carbon mapping in rainforest but large scale forest inventories can help to overcome
Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.
Data from: Spatial structure of above-ground biomass limits accuracy of carbon mapping in rainforest but large scale forest inventories can help to overcome
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Water availability drives above-ground biomass and bird richness in forest restoration plantings to achieve carbon and biodiversity co-benefits
Open the record for dataset details and reuse information.
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