DSM and DTM generation from VHR satellite stereo imagery over plastic covered greenhouse areas
Agriculture under Plastic Covered Greenhouses (PCG) has represented a step forward in the evolution from traditional to industrial farming. However, PCG-based agricultural model has been also criticized for its associated environmental impact such as plastic waste, visual impact, soil pollution, bio...
| Main Authors: | , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
Computers and Electronics in Agriculture (Elsevier)
2023
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/14779 https://doi.org/10.1016/j.compag.2019.104903 |
| Summary: | Agriculture under Plastic Covered Greenhouses (PCG) has represented a step forward in the evolution from traditional to industrial farming. However, PCG-based agricultural model has been also criticized for its associated environmental impact such as plastic waste, visual impact, soil pollution, biodiversity degradation and local runoff alteration. In this sense, timely and effective PCG mapping is the only way to help policy-makers in the definition of plans dealing with the trade-off between farmers’ profit and environmental impact for the remaining inhabitants.
This work proposes a methodological pipeline for producing high added value 3D geospatial products (Digital Surface Models (DSM) and Digital Terrain Models (DTM)) from VHR satellite imagery over PCG areas. The 3D information layer provided through the devised approach could be very valuable as a complement to the traditional 2D spectral information offered by VHR satellite imagery to improve PCG mapping over large areas.
This methodological approach has been tested in Almeria (Southern Spain) from a WorldView-2 VHR satellite stereo-pair. Once grid spacing format DSM and DTM were built, their vertical accuracy was assessed by means of lidar data provided by the Spanish Government (PNOA Programme).
Regarding DSM completeness results, the image matching method based on hierarchical semi-global matching yielded much better scores (98.87%) than the traditional image matching method based on area-based matching and cross-correlation threshold (86.65%) when they were tested on the study area with the highest concentration of PCG (around 85.65% of PCG land cover). However, both image matching methods yielded similar vertical accuracy results in relation to the finally interpolated DSM, with mean errors ranging from 0.01 to 0.35m and random errors (standard deviation) between 0.56 and 0.82 m. The DTM error figures also showed no significant differences between both image matching methods, although being highly dependent on DSM-to- DTM filtering error, in turn closely related to greenhouse density and terrain complexity. |
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