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| 2. |
- Duncanson, Laura, et al.
(författare)
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Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- 2022
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 270
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Tidskriftsartikel (refereegranskat)abstract
- NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
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| 3. |
- Kissling, W. Daniel, et al.
(författare)
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Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale
- 2018
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Ingår i: Biological Reviews. - : Wiley. - 1464-7931 .- 1469-185X. ; 93:1, s. 600-625
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Tidskriftsartikel (refereegranskat)abstract
- © 2017 Cambridge Philosophical Society. Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.
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| 4. |
- Skidmore, Andrew K., et al.
(författare)
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Comment on "The global tree restoration potential"
- 2019
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Ingår i: Science (New York, N.Y.). - : American Association for the Advancement of Science (AAAS). - 1095-9203 .- 0036-8075. ; 366:6469
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Tidskriftsartikel (refereegranskat)abstract
- Bastin et al (Reports, 5 July 2019, p. 76) claim that 205 gigatonnes of carbon can be globally sequestered by restoring 0.9 billion hectares of forest and woodland canopy cover. Reinterpreting the data from Bastin et al, we show that the global land area actually required to sequester human-emitted CO2 is at least a factor of 3 higher, representing an unrealistically large area.
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| 5. |
- Skidmore, Andrew K., et al.
(författare)
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Geospatial tools address emerging issues in spatial ecology: a review and commentary on the Special Issue
- 2011
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Ingår i: International Journal of Geographical Information Science. - : Informa UK Limited. - 1365-8824 .- 1365-8816 .- 1362-3087. ; 25:3, s. 337-365
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Forskningsöversikt (refereegranskat)abstract
- Spatial ecology focuses on the role of space and time in ecological processes and events from a local to a global scale and is particularly relevant in developing environmental policy and (mandated) monitoring goals. In other words, spatial ecology is where geography and ecology intersect, and high-quality geospatial data and analysis tools are required to address emerging issues in spatial ecology. In this commentary and review for the International Journal of GIS Special Issue on Spatial Ecology, we highlight selected current research priorities in spatial ecology and describe geospatial data and methods for addressing these tasks. Geoinformation research themes are identified in population ecology, community and landscape ecology, and ecosystem ecology, and these themes are further linked to the assessment of ecosystem services. Methods in spatial ecology benefit from explicit consideration of spatial autocorrelation, and applications discussed in this review include species distribution modeling, remote sensing of community and ecosystem properties, and models of climate change. The linkages of the Special Issue papers to these emerging issues are described.
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