Genya Saito

Crop discrimination with multitemporal SPOT/HRV data in the Saga Plains, Japan

Nine scenes of SPOT/HRV data obtained in eight different months in 1997 were evaluated for crop discrimination in the Saga Plains, Japan. All images were atmospherically corrected with the 6S code. Annual Normalized Difference Vegetation Index (NDVI) profiles were generated to characterize seasonal trends in six cropping systems (rice, rice-winter cereal, soybean, soybean-winter cereal, lotus, and rush). The dataset of this study showed the unique temporal change patterns of NDVI for each cropping system. Separability analyses determined optimal scene combinations for the highest accuracy in classifying the cropping systems. The scene combinations for the accurate classification of cropping systems were obtained from three separability measurements (Euclidean spectral distance, divergence, and Jeffries-Matsushita distance). Kappa statistics were applied to evaluate the classification accuracies. The four-scene combination that was derived from April, June, July and September classified the cropping systems almost as well as those combinations including more scenes. A colour composition technique applied to the three-scene combination that showed the highest separability also discriminated each cropping system. Based on these results, we can request observations during specific time intervals considering local crop calendars and environmental conditions.

Characterization of Rice Paddies by a UAV-Mounted Miniature Hyperspectral Sensor System

A low-cost, small, lightweight hyperspectral sensor system that can be loaded onto small unmanned autonomous vehicle (UAV) platforms has been developed for the acquisition of aerial hyperspectral data. Safe and easy observation is possible under unstable illumination conditions by using lightweight and autonomous cruising. The hyperspectral sensor system, equipped with a 256-band hyperspectral sensor covering a spectral range from 340-763 nm, a GPS and a data logger, is 400 g in total weight. The acquisition period for each sampling, 768 bytes, is 100 ms. The aerial hyperspectral data of rice paddies are collected under cloudy weather. The flight altitude from the ground is 10 m, and the cruising speed is 2 m/s. The high-accuracy estimation of the chlorophyll densities is confirmed, even under unstable illumination conditions, by frequent monitoring of the illumination level and the chlorophyll indices, based on the red-edge (RE) and near infrared (NIR) spectral ranges.

Polarimetric Decomposition Analysis of ALOS PALSAR Observation Data before and after a Landslide Event

Radar scattering mechanisms over landslide areas were studied using representative full polarimetric parameters: Freeman–Durden decomposition, and eigenvalue–eigenvector decomposition. Full polarimetric ALOS (Advanced Land Observation Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) datasets were used to examine landslides caused by the 2008 Iwate-Miyagi Nairiku Earthquake in northern Japan. The Freeman–Durden decomposition indicates that areas affected by large-scale landslides show dominance of the surface scattering component in both ascending and descending orbit data. The polarimetric parameters of eigenvalue–eigenvector decomposition, such as entropy, anisotropy, and alpha angle, were also computed over the landslide areas. Unsupervised classification based on the H- plane explicitly distinguishes landslide areas from others such as forest, water, and snow-covered areas, but does not perform well for farmland. A landslide area is difficult to recognize from a single-polarization image, whereas it is clearly extracted on the full polarimetric data obtained after the earthquake. From these results, we conclude that 30-m resolution full polarimetric data are more useful than 10-m resolution single-polarization PALSAR data in classifying land coverage, and are better suited to detect landslide areas. Additional information, such as pre-landslide imagery, is needed to distinguish landslide areas from farmland or bare soil.

Growth monitoring and classification of rice fields using multitemporal RADARSAT-2 full-polarimetric data

Spaceborne synthetic aperture radar (SAR) can be used for agricultural monitoring. In this study, three single-polarimetric and four full-polarimetric observation data sets were analysed. A rice paddy field in northern Japan was used as the study site; the data for this site were obtained using RADARSAT-2, which carries a full-polarimetric C-band SAR. Soybean and grass fields were also present within the paddy fields. The temporal change in the backscattering coefficient of the rice paddy fields for the single-polarization data agreed with the temporal change obtained for a rice growth model based on radiative transfer theory. A three-component decomposition approach was applied to the full-polarimetric data. With each rice growth stage, the volume scattering component ratio increased, whereas the surface scattering component ratio generally decreased. The soybean and grass fields showed a smaller double-bounce scattering component than the rice fields for all the acquired data. The results of this study show that multitemporal observation by full-polarimetric SAR has great potential to be utilized for estimating rice-planted areas and monitoring rice growth.

Satellite monitoring of changes in improved grassland management

In this study, the satellite monitoring of improved grassland management is discussed. In the satellite images acquired during the grass cutting season, mown grasslands show distinctive reflectance characteristics. Using these characteristics, we have analysed changes in grassland use and grass mowing strategies in a dairy farming region of Japan during the past decade. Our analysis shows that the amount of grazing pasture has decreased in the study site, while the timing of mowing the grass has improved. A ground survey of grassland use and grass mowing time in the study site shows the same tendencies. This agreement indicates that satellite remote sensing is an effective tool for monitoring the status of grassland management.

On the Bragg Scattering Observed in L-Band Synthetic Aperture Radar Images of Flooded Rice Fields

This article presents the analysis of the Bragg scattering phenomenon which has been observed in the images of machine-planted rice paddies acquired by the JERS-1 L-band synthetic aperture radar (SAR). The simultaneous measurements of rice plants were made at the SAR data acquisition times. Large differences of 20-25 dB in image intensity between the transplanting and ripening stages are found to be dependent on the planting direction and bunch separation. This selective image enhancement is a result of the Bragg resonance backscatter due to the double-bounce of incident L-band microwave between the flooded water surface and periodically planted bunches of rice plants. Support for the idea of double-bounce scattering is provided by the decomposition analysis of L-band and X-band polarimetric Pi-SAR data; and a simple numerical simulation based on the physical optics model shows fairly good agreement with the JERS-1 SAR data. The results presented in this paper is mainly of academic interest, but a suggestion can be made on the selection of suitable microwave band for monitoring rice fields.

Salt-damaged paddy fields analyses using high-spatial-resolution hyperspectral imaging system

In agricultural fields, the damage caused by salinized winds is crucial for crops. In order to minimize the damage, it is required to detect the damaged areas and enact the proper procedures in order to save the damaged fields immediately after the disaster. In this paper, we propose indices that can indicate the degree of salt-breezed damages in the early withering-up stage. To detect the indices, high-spatial-resolution hyperspectral data taken in actual damaged paddy fields are analyzed. In addition, the sequential change of hyperspectral data in rice within artificial withering-up experiments is recorded to interpret the fundamental mechanism of the indices. The applicability of the indices for satellite data is also shown by applying them to simulated satellite data. Keywords-component; spectroscopy; rice paddy; hyperspectral; salt-damaged; SPOT5; NDVI; NDGI; withering up;

Development of a Low-Cost, Lightweight Hyperspectral Imaging System Based on a Polygon Mirror and Compact Spectrometers

Low-altitude hyperspectral observation systems using aerial observation with unmanned aerial vehicles (UAVs) have advantages over satellite systems with respect to frequency, accuracy, and spatial resolution. Although low-cost lightweight UAVs have become available in recent years, the current price ranges of lightweight pushbroom and snapshot hyperspectral sensors remain high. For sustainable operation of UAV-mounted hyperspectral sensing, the challenge in production has been shifted from the size and weight to the cost of the lightweight hyperspectral sensors. In this paper, we develop a low-cost, lightweight whiskbroom hyperspectral imaging system. The gross weight of the sensor is 1200 g. The spectral range of the 256-band spectrometer extends from 340 to 750 nm with a 14-nm spectral resolution. The viewing angle across the flight direction is controlled by the rotation of an eight-sided polygon mirror. When the exposure time, flight altitude, flight speed, and focal length of the optical lens are 3.2 ms, 10 m, 10 m/s, and 8 mm, respectively, then the estimated values of the swath and the area coverage per second are 13.4 m and 134.1

Single‐look classification accuracy for polarimetric SAR

\hbox{m}^2

Semi-Supervised Hyperspectral Subspace Learning Based on a Generalized Eigenvalue Problem for Regression and Dimensionality Reduction

/s, respectively. The spatial resolution is 0.97 (m) (flight direction)