Abstract
Monitoring grassland vegetation growth is of vital importance to scientific grazing and grassland management. People expect to be able to use a portable device, like a mobile phone, to monitor grassland vegetation growth at any time. In this paper, we propose a handheld grassland vegetation monitoring system to achieve the goal of monitoring grassland vegetation growth. The system includes two parts: the hardware unit is a hand-held multispectral imaging tool named ASQ-Discover based on a smartphone, which has six bands (wavelengths)—including three visible bands (450 nm, 550 nm, 650 nm), a red-edge band (750 nm), and two near-infrared bands (850 nm, 960 nm). The imagery data of each band has a size of 5120 × 3840 pixels with 8-bit depth. The software unit improves image quality through vignetting removal, radiometric calibration, and misalignment correction and estimates and analyzes spectral traits of grassland vegetation (Fresh Grass Ratio (FGR), NDVI, NDRE, BNDVI, GNDVI, OSAVI and TGI) that are indicators of vegetation growth in grassland. We introduce the hardware and software unit in detail, and we also experiment in five pastures located in Haiyan County, Qinghai Province. Our experimental results show that the handheld grassland vegetation growth monitoring system has the potential to revolutionize the grassland monitoring that operators can conduct when using a hand-held tool to achieve the tasks of grassland vegetation growth monitoring.
Highlights
Grassland is one of the most important terrestrial resources in the world, and it is the basis for the livelihood of herdsmen
4b Removal is the results of vignetting correction using the method proposed in this
The vignetting phenomenon is related to the structure parameters of the camera, 3.1
Summary
Grassland is one of the most important terrestrial resources in the world, and it is the basis for the livelihood of herdsmen. They are often used to assess grassland quality and productivity, they require destructive sampling and involve highly sophisticated laboratory-based experiments This is not desirable as destructive sampling prevents the monitoring of the vegetation growth over time. Multispectral imaging plays an important role in grassland monitoring [4,5,6,7,8,9], and multispectral images are often used to establish some quantitative retrieval models of grassland biomass and vegetation nutrition contents. Grassland is a complex dynamic system, the quantitative retrieval models of grassland biomass and vegetation nutrition contents should be different in different stages of a growth cycle, and they should be different in different geographical locations.
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