Abstract
When an analysis of physical quantity of vegetation using a satellite remote-sensing
technology, it is very effective for monitoring of a global area. However, these satellite data
include many errors by an atmosphere, a sun-target-sensor geometry, sensor characteristics and
so on. BRDF (Bi-directional Reflectance Distribution Factor) is one of them. And almost
researcher should not use that, and except that. We are understood that if we corect BRDF error
for satellite data, we need some geometric information, LAI, and another information. We
changed our thought. Using BRDF characteristics for satellite data analysis, we will possible to
obtain some information for satellite data. This study is first step of using BRDF analysis for
satellite.
1. Introduction
It is (1999, Yamamoto etc.) that is understanding that is estimated with a high
correlation that the information of a height of grass is adopted, in the case that estimate biomass
and calculate NDVI more the data of the spectrum reflection rate that was obtained with
location observation. However, in the case that this is adapted to the satellite sensor the
information of the height of grass is a present condition to be estimating biomass from only
NDVI, without being proposed to estimate from the satellite.
The information etc. of the position information, the sun of the reflection rate, sensor
of the reflection rate, soil of a/the height of grass, stock number, plain are being substituted, in
the BRDF model that Watanabe etc. proposed in 1998. It is make the multidimensional
simultaneous equations of the coefficient such as grass canopy height, stock numbers that can
not estimate this model, from the satellite sensor such as grass canopy height, stock several,
that I use several bands of the many channel sensors of GLI, MODIS etc.
2. Objectives
The main purposes of this study are as follows:
- A study of relationship between BRDF and Grass Canopy Height for ground truth data.
- Estimation of Grass Canopy Height using its BRDF Property
3. Methods
Ground truth
We measured BRDF using our original system (Fig. 2.1 and 2.2) in 1998 and 1999.
The measurement areas were located in Mongolian grassland. The areas were homogeneity, and
not influenced by geographical (Fig 2.3). In 1998, there was much rain, therefore vegetation
condition was very good. And grasses had grown, while we were measuring. However, in 1999,
there was few rain. And growth of grasses was not so good.
However, we were obtain data of deferent grass canopy height at same measurement
area. And we compared with those data, and study relationship between BRDF and grass
canopy height. Table 2.1 is obtained and using data for study relationship.
fig. 2.1 BRDF Measurement System
Fig. 2.2 Sensor Mount
Table 2.1 List of using Field Data
| Date |
Vegetation Coverage (%) |
Grass Height (cm) |
| 1998/8/9 |
15% |
7.72 |
| 1998/8/10 |
5.40% |
6.65 |
| 1998/8/11 |
7.90% |
8.3 |
| 1998/8/19 |
9% |
4.45 |
| 1998/8/22 |
4% |
6.31 |
