Research background and significance
Hyperspectral remote sensing technology makes it possible to obtain plant chemical information. It can carefully analyze various biochemical information of grass leaves and the changing trends of grass in time and space, and then determine the production capacity, production rate, decomposition rate of plants after withering, and various nutrients of grasslands. These monitoring results can be used to comprehensively evaluate the growth of grassland grass, provide a scientific basis for reasonable fertilization and optimization of grassland management measures, and promote the healthy growth and sustainable development of grasslands.
Introduction to equipment application
1. Grassland biomass estimation: Accurately estimate the available amount: measure common parameters of grassland biomass such as normalized difference vegetation index (NDVI) and red edge (REP). After the emergence of hyperspectral remote sensing technology, NDVI has more band options to represent grassland vegetation information, and the red edge is relatively more stable. Accurately estimating the available amount of grassland can help farmers reasonably control the amount of livestock raised, ensure that grassland and animal husbandry are in a dynamic balance, and achieve sustainable utilization of grassland resources.
2. Assist in model construction and optimization: Using the hyperspectral remote sensing model method, single variable linear and nonlinear regression models and stepwise regression models can be used to accurately estimate grassland biomass, and then determine the optimal model of the original hyperspectral band variables, providing data support and decision-making basis for the scientific management and planning of grassland resources.
3. Grassland species identification: Improve identification and classification accuracy: Hyperspectral data obtained by hyperspectral remote sensing technology can improve the ability to identify and classify plant species. Staff can select from specific narrow bands for analysis of large differences, and combine the separated specific bands to automatically identify and quantitatively analyze grassland forage species and poisonous weed species. Data compression technology can also be used to conduct a comprehensive analysis of several specific bands and make them into a specific band complex to improve the utilization rate of information data and more accurately distinguish forage species.
