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Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands

Overview of attention for article published in Carbon Balance and Management, April 2017
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Title
Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands
Published in
Carbon Balance and Management, April 2017
DOI 10.1186/s13021-017-0076-y
Pubmed ID
Authors

Mikael Egberth, Gert Nyberg, Erik Næsset, Terje Gobakken, Ernest Mauya, Rogers Malimbwi, Josiah Katani, Nurudin Chamuya, George Bulenga, Håkan Olsson

Abstract

Soil carbon and biomass depletion can be used to identify and quantify degraded soils, and by using remote sensing, there is potential to map soil conditions over large areas. Landsat 8 Operational Land Imager satellite data and airborne laser scanning data were evaluated separately and in combination for modeling soil organic carbon, above ground tree biomass and below ground tree biomass. The test site is situated in the Liwale district in southeastern Tanzania and is dominated by Miombo woodlands. Tree data from 15 m radius field-surveyed plots and samples of soil carbon down to a depth of 30 cm were used as reference data for tree biomass and soil carbon estimations. Cross-validated plot level error (RMSE) for predicting soil organic carbon was 28% using only Landsat 8, 26% using laser only, and 23% for the combination of the two. The plot level error for above ground tree biomass was 66% when using only Landsat 8, 50% for laser and 49% for the combination of Landsat 8 and laser data. Results for below ground tree biomass were similar to above ground biomass. Additionally it was found that an early dry season satellite image was preferable for modelling biomass while images from later in the dry season were better for modelling soil carbon. The results show that laser data is superior to Landsat 8 when predicting both soil carbon and biomass above and below ground in landscapes dominated by Miombo woodlands. Furthermore, the combination of laser data and Landsat data were marginally better than using laser data only.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 8 17%
Student > Master 7 15%
Student > Bachelor 6 13%
Researcher 6 13%
Professor 3 7%
Other 8 17%
Unknown 8 17%
Readers by discipline Count As %
Environmental Science 14 30%
Agricultural and Biological Sciences 7 15%
Earth and Planetary Sciences 6 13%
Engineering 3 7%
Computer Science 1 2%
Other 3 7%
Unknown 12 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 April 2017.
All research outputs
#5,492,574
of 9,734,985 outputs
Outputs from Carbon Balance and Management
#98
of 128 outputs
Outputs of similar age
#146,450
of 261,819 outputs
Outputs of similar age from Carbon Balance and Management
#5
of 5 outputs
Altmetric has tracked 9,734,985 research outputs across all sources so far. This one is in the 26th percentile – i.e., 26% of other outputs scored the same or lower than it.
So far Altmetric has tracked 128 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.0. This one is in the 10th percentile – i.e., 10% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 261,819 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.