Abstract
Abstract. The citizen science program to supplement authoritative data in tree inventory has been well implemented in various countries. However, there is a lack of study that assesses correctness and accuracy of tree data supplied by citizens. This paper addresses the issue of tree data quality supplied by semi-literate indigenous group. The aim of this paper is to assess the correctness of attributes (tree species name, height and diameter at breast height) and the accuracy of tree horizontal positioning data supplied by indigenous people. The accuracy of the tree horizontal position recorded by GNSS-enable smart phone was found to have a RMSE value of ± 8m which is not suitable to accurately locate individual tree position in tropical rainforest such as the Royal Belum State Park. Consequently, the tree species names contributed by indigenous people were only 20 to 30 percent correct as compared with the reference data. However, the combination of indigenous respondents comprising of different ages, experience and knowledge working in a group influence less attribute error in data entry and increase the use of free text rather than audio methods. The indigenous community has a big potential to engage with scientific study due to their local knowledge with the research area, however intensive training must be given to empower their skills and several challenges need to be addressed.
Highlights
Tropical forest is one of the most complex ecosystems exist on Earth and has become a habitat of more than 50% of known species of flora and fauna
The purpose of task 1 was to assess whether indigenous people could insert data accurately into a mobile data collector (in a condition of accurate reference data were given)
The root mean square errors (RMSE) of tree positions recorded by the smartphone were calculated against the reference positioning data
Summary
Tropical forest is one of the most complex ecosystems exist on Earth and has become a habitat of more than 50% of known species of flora and fauna. Many researchers have implemented the usage of remote sensing technique such as airborne LiDAR, unmanned aerial vehicle (UAV), terrestrial laser scanning (TLS) and optical satellite data (Bauwens et al, 2016; Karila et al, 2015; Liu et al, 2014; Rahfl et al, 2014; Solberg et al, 2015). These techniques relies on estimation and calculation (Liang et al ,2012), ground truth data collection is still required in checking and producing correct and valid data
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