An updated survey on the use of geospatial technologies in New Zealand’s plantation forestry sector (2020)
Authorsde Gouw S, Morgenroth J, Xu Cshow all
Background: Geospatial technologies have developed rapidly in recent decades and can provide detailed, accurate data to support forest management. Knowledge of the uptake of geospatial technologies, as well as barriers to adoption, in New Zealand’s plantation forest management sector is limited and would be beneficial to the industry. This study provides an update to the 2013 benchmark study by Morgenroth and Visser.
Methods: An online survey was sent to 29 companies that own or manage plantation forests in New Zealand. The survey was split into seven sections, composed of multiple-choice and open-ended questions, on the topics of: demographic information, data portals and datasets, global navigation satellite system (GNSS) receivers, and four remote-sensing technologies. These included aerial imagery, multispectral imagery, hyperspectral imagery, and light detection and ranging (LiDAR). Each section included questions relating to the acquisition, application and products created from each remote-sensing technology. Questions were also included that related to the barriers preventing the uptake of technologies. To determine the progression in the uptake of these technologies the results were compared to Morgenroth and Visser's study conducted five years' earlier.
Results: Twenty-three companies responded to the survey and together, those companies managed approximately 1,172,000 ha (or 69% of New Zealand’s 1.706 million ha plantation forest estate (NZFOA, 2018)). The size of the estates managed by individual companies ranged from 1,000 ha to 177,000 ha (quartile 1 = 19,000 ha, median = 33,000 ha, quartile 3 = 63,150 ha). All companies used GNSS receivers and acquired three-band, Red-Green-Blue, aerial imagery. Multispectral imagery, hyperspectral imagery and LiDAR data were acquired by 48%, 9% and 70% of companies, respectively. Common applications for the products derived from these technologies were forest mapping and description, harvest planning, and cutover mapping. The main barrier preventing companies from acquiring most remotely-sensed data was the lack of staff knowledge and training, though cost was the main barrier to LiDAR acquisition. The uptake of all remote-sensing technologies has increased since 2013. LiDAR had the largest progression in uptake, increasing from 17% to 70%. There has also been a change in the way companies acquired the data. Many of the companies used unpiloted aerial vehicles (UAV) to acquire aerial and multispectral imagery in 2018, while in 2013 no companies were using UAVs. ESRI ArcGIS continues to be the dominant geographic information system used by New Zealand’s forest management companies (91%), though 22% of companies now use free GIS software, like QGIS or GRASS. The use of specialised software (e.g. FUSION, LAStools) for LiDAR or photogrammetric point cloud analysis increased since 2013, but most forestry companies who are processing .las files into various products (e.g. digital terrain model) are using ArcGIS.
Conclusions: This study showed that there had been a progression in the uptake of geospatial technologies in the New Zealand plantation forest management sector. However, there are still barriers preventing the full utilisation of these technologies. The results suggest that the industry could benefit from investing in more training relating to geospatial technologies.
Citationde Gouw S, Morgenroth J, Xu C (2020). An updated survey on the use of geospatial technologies in New Zealand’s plantation forestry sector. New Zealand Journal of Forestry Science. 50.
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KeywordsGeospatial technologies; GNSS; GPS; remote sensing; GIS; forestry; education; UAV
ANZSRC Fields of Research
30 - Agricultural, veterinary and food sciences::3007 - Forestry sciences::300707 - Forestry management and environment
40 - Engineering::4013 - Geomatic engineering::401399 - Geomatic engineering not elsewhere classified
46 - Information and computing sciences::4601 - Applied computing::460106 - Spatial data and applications