11:00 〜 13:00
[MSD41-P04] リモートセンシング技術を用いた牧草地の植物種マップの作成
キーワード：リモートセンシング、農業、高地上分解能、牧草
Raw milk accounts for about 10% of the agricultural output in Japan. Despite growing demand for raw milk, milk production has not shown much increase. This is due to the aging of dairy farmers, which has resulted in a serious succession problem. The number of dairy farmers has declined by about one-thirtieth, from a peak of 418,000 in 1959 to 15,000 in 2019, with about 700 dairy farms closing down annually in recent years. In order to ensure a stable supply of milk, it is necessary to prevent a decline in milk production. Feed self-sufficiency rate for daily is declining year by year due to the high supply of imported concentrates feed. Currently, about half of the meadows in Hokkaido are occupied by gramineous weeds. High occupancy of weeds is one of the factors that reduces the raw milk production. The detection of weeds in meadow using remote sensing technology has been attracting attention in recent years. As for broadleaf weeds, measurement with high ground resolution makes it possible to identify weed locations using image recognition technology. However, most of weeds look very similar to the grass and are very difficult to identify visually. It is necessary to estimate the occupancy rate of gramineous weeds by observation and to promote meadow renovation. In previous studies, observations by remote sensing techniques were made using broadband filters with a ground resolution of 3 m and FWHM of 50-100 nm, which made it impossible to discriminate between grasses and gramineous weeds with similar reflection spectra with sufficient accuracy.
In this study, we developed a new method for identifying plant species with four narrow-band filters and made a plant species map of the meadows by drone measurement. First, the spectral reflectance for 6 species of grass and weeds, which are common in meadows in Hokkaido, was measured by a hyperspectral camera at wavelength resolution of ~4nm in the range of 420-840nm. 4 narrow bands were selected from the whole measured wavelengths for the plant species identification. A tree diagram for identifying plant species was made based on those 4 band measurements. Next, two meadows in Hokkaido have measured with a multi-band camera with 4 selected filters (FWHM of 10nm) onboarded a drone. Pure communities of 4 species of plants as the reference data were observed on the same day of ground validation. A plant species map was created by synthesizing the images taken by the drone and by color-coding them according to the tree diagram.
A tree diagram with two conditional branches was developed. The differences in the visible light were emphasized by normalizing the reflectance as the first conditional branch. In addition, by using timothy as a standard sample and taking the difference from this, the differences between plant species were emphasized as the second conditional branch. From these emphasized differences, two conditional branches were performed and the plant species were successfully identified.
The plant species map for the timothy, orchard grass, and reed canary grass was created. First, timothy in the meadow was colored red and orchard grass, and reed canary grass was colored blue based on the first conditional branch of the tree diagram. Next, reed canary grass colored green based on the second conditional branch. The plant species map with three colors was successfully identified each pure community.
In conclusion, our new method enables us to identify plant species in meadows by drone measurement.
In this study, we developed a new method for identifying plant species with four narrow-band filters and made a plant species map of the meadows by drone measurement. First, the spectral reflectance for 6 species of grass and weeds, which are common in meadows in Hokkaido, was measured by a hyperspectral camera at wavelength resolution of ~4nm in the range of 420-840nm. 4 narrow bands were selected from the whole measured wavelengths for the plant species identification. A tree diagram for identifying plant species was made based on those 4 band measurements. Next, two meadows in Hokkaido have measured with a multi-band camera with 4 selected filters (FWHM of 10nm) onboarded a drone. Pure communities of 4 species of plants as the reference data were observed on the same day of ground validation. A plant species map was created by synthesizing the images taken by the drone and by color-coding them according to the tree diagram.
A tree diagram with two conditional branches was developed. The differences in the visible light were emphasized by normalizing the reflectance as the first conditional branch. In addition, by using timothy as a standard sample and taking the difference from this, the differences between plant species were emphasized as the second conditional branch. From these emphasized differences, two conditional branches were performed and the plant species were successfully identified.
The plant species map for the timothy, orchard grass, and reed canary grass was created. First, timothy in the meadow was colored red and orchard grass, and reed canary grass was colored blue based on the first conditional branch of the tree diagram. Next, reed canary grass colored green based on the second conditional branch. The plant species map with three colors was successfully identified each pure community.
In conclusion, our new method enables us to identify plant species in meadows by drone measurement.