Detecting management practices of improved grasslands
using ERS-1 SAR data
Nobuyuki Mino, Genya Saito and Shigeo Ogawa
National Institute of Agro-Environmental Sciences,
3-1-1 Kannondai, Tsukuba, Ibaraki 305 Japan
Tel : (81)-298-38-8225 Fax: (81)-298-38-8199
E-mail:minonobu@niaes.affrc.go.jp
Abstract
The backscattering behavior of the improved grasslands is investigated with the ERS-1 SAR data of a dairy farming area in Japan. Our results show that renovated grasslands with high surface roughness have extremely high backscattering coefficient, whereas the others do not show such high coefficients. The SAR-derived grass renovation map using backscattering signatures is almost consistent with the results from Landsat TM data. Grass renovation is a very important procedure for improving grasslands productivity. In dairy farming areas where optical data often hindered, the success in monitoring grass renovation using ERS-1 SAR data is considered to be very useful.
Introduction
Many dairy farming regions are distributed in the northern part of Japan and are almost perennial cloud cover rather than in upland crop areas such as paddy fields. Although optical Remote Sensing is greatly useful for monitoring improved grasslands, temporal monitoring is very difficult because optical data are hard to acquire. Space borne SAR measurements overcome this limitation of optical sensing and are expected to e an alternative method for grassland monitoring. In this study, we discuss the capabilities of ERS-1 SAR and its usefulness in monitoring grasslands management practices.
Material and methods
1. Management practices of improved grasslands
Grass-renovation and grass mowing are major management practices of improved grasslands. Precise and wide-area monitoring of them greatly contribute to decision making in management strategies. Grass-renovation is one of the most effective methods to recover grassland productivity. This practice consists of ploughing and Remote Sensing-seeding, usually it is applied to older grasslands which can not be improved by fertilizer applications. Although agricultural experimental station suggests that it should be applied once per 7-8 years, frequent implement of grass-renovation methods does not occur. Because grass-age (years after grass-renovation) is one good measure of productivity, the renovated year is a very useful guide for various management practices. The timing of grass mowing is also important for obtaining nutrient rich grass production. Because total nutrient amount decreases after the heading stage, mowing just prior to the heading have been recommended. Recently, with an increased demand for high quality grass production, the mowing time has been shifted to optimize productivity. Thus, monitoring of the status of grass mowing leads to a better understanding of management practices. In this study, we aimed to identify those two management procedures, grass-renovation and grass mowing, using ERS-1 SAR data.
2. Approach
Figure 1 shows a flow chart of the data and processing steps used in this study. During the growing season, grasslands have a considerable amount of biomass, however both renovated and mown grasslands have no-biomass. Renovated and mown grasslands have great differences in ground surface conditions. Renovated grasslands are characterized by the presence of bare soil and high roughness due to ploughing. Whereas, mown grasslands have the accumulation of dead material and no-ploughing activities. These distinctive characteristics of grasslands can be identify using optical data. Data characteristics of the Landsat TM image of unmown, mown and renovated grasslands are shown in figure 2. Although figure 2 does not show data for grazing pastures, characteristics almost similar to unmown grasslands are shown. Identification of backscattering signatures of unmown, mown and renovated grasslands on ERS-1 SAR data can be made, using optical sensors and ERS-1 SAR data observed on the almost the same date. In this study, we asses the capability of ERS-1 SAR data to discriminate the three conditions of grasslands using the Landsat TM-ERS-1 SAR data pairs.
Figure1.Flow chart of the used data and processing steps in this study
Figure 2. Landsat TM data (17 August 1995) signatures of mown, unmown and renovated grasslands.
