APPLICATION OF HYPERION DATA TO AGRICULTURAL LAND CLASSIF ICATION
AND VEGETATION PROPERTIES ESTIMATION IN SWITZERLAND
S. Eckert, M. Kneubühler
? Remote Sensing Laboratories (RSL), University of Ziirich, Winterthurerstrasse 190, 8057 Ziirich, Switzerland -
(seckert, kneubd@geo.unizh.ch
WG VII/1 — Fundamental Physics and Modelling (TS)
KEY WORDS: Agriculture, Land Cover, Classification, Remote Sensing, Hyper Spectral, Object, Vegetation, Contextual
ABSTRACT:
On August 18, 2002, the Hyperion hyperspectral imager onboard the EO-1 platform recorded data over an intensively used
agricultural area in north western Switzerland, the Limpach Valley. The sensor’s 198 spectral bands between 400 and 2500 nm
(Level 1B1) and a spatial resolution of 30 m bear the potenti
al for both a detailed land use classification and an accurate
estimation of biophysical and biochemical properties of heterogeneously vegetated areas. This study evaluates the potential
of HYPERION data for land use classification and vegetation properties estimation (e.g., LAI) in a typical Swiss agricultural
environment with its small-spaced fields. A Spectral Angle Mapper approach and a multi-scale object-oriented method are
applied for agricultural land use determination. The results show, that the phenological stages of the cultivars are the main
factors influencing the separability of agricultural cl
1. INTRODUCTION
1.1 Overview
The growing need for quantitative studies on
biogeophysical and -chemical processes in vegetation
analysis for agricultural purposes on the one hand, and
ecosystem functioning on the other hand, imply both higher
spectral and spatial resolution of spaceborne remote sensing
devices, together with improved radiometric performance
and accurate geolocation. The HYPERION sensor onboard
NASA's Earth Observing 1 (EO-1) satellite is the first
spaceborne hyperspectral instrument to acquire both
visible/near-infrared (400-1000 nm) and shortwave infrared
(900-2500 nm) spectral data. With its 242 potential bands
and a spatial resolution of 30 m, the sensor bears the
potential to provide data for both a detailed land use
classification and an accurate estimation of biogeophysical
and —chemical properties of heterogeneously ‘vegetated
areas.
In this study, the suitability of HYPERION data for land use
classification and vegetation properties estimation in a
typical Swiss agricultural environment with its small-spaced
fields is evaluated. Land use determination from spectral
data is performed using both a well established
hyperspectral approach (Spectral Angle Mapper) and a multi-
scale object-oriented method which allows to derive
meaningful image segments on the one hand and to describe
the segment’s physical and contextual characteristics on the
other hand. The potential of the hyperspectral dataset for
vegetation properties estimation (e.g., LAI) within single
cultivars is approached by assessing the spectral variability
of dedicated fields.
1.2 Study Site Location
The Limpach Valley, being the study site of this work, is an
intensively cultivated agricultural area in northwestern
Switzerland with more than 2000 individual fields. The
climate of the Limpach Valley can be regarded as typical for
the Swiss Midlands. Precipitation is even to moderatly dry.
asses and therefore determining the accuracies of the methods applied.
The vegetation period lasts between 210-230 days. The
summer months are characterized by hot temperatures and
occasional dry periods [Kneubiihler, 2002]. The main
agricultural cultivars in the area are maize, potatoes, wheat,
barley, canola and sugar beet. Besides, both intensively and
extensively used types of grassland can be found. In August,
after harvest of the majority of the cereals, the visual
impression of the valley is dominated by stubble-fields and
bare soil. A detailed ground-truth dataset consisting of more
than 50 fields was recorded in the field in order to verify the
results of the two land use determination approgghes
performed in this study. However, in the ground-truth data
some crops are represented by only two or three fields.
1.3 HYPERION Hyperspectral Data
HYPERION data were acquired over the Limpach Valley test
site on August 18, 2002 at 09:05:42 UTC. The EO-1 satellite
is in a sun-synchronous orbit at 705 km altitude. HYPERION
images 256 pixels with a nominal size of 30 m on the
ground over a 7.65 km swath. Well-calibrated data (Level
1B1) is routinely available. Post-Level IBI processing of
the dataset, as performed in this study, includes correction
for striping pixels, atmospheric correction and
georectification.
HYPERION data is acquired in pushbroom mode with two
spectrometers. One operates in the VNIR range (70 bands
between 356-1058 nm with an average FWHM of 10.90 nm)
and the other in the SWIR range (172 bands between 852-
2577 nm, with an average FWHM of 10.14 nm). Of the 242
Level 1B1 bands, 44 are set to zero by software during Level
IB1 processing (bands 1-7, 58-76, 225-242).
2. REMOTE SENSING DATA PREPARATION
Post-Level 1B1 data processing of the acquired HYPERION
scene contains correction for striping pixels, a scene-based
atmospheric correction using ATCOR-4 [Richter] and à
georectification procedure, as described in this section.
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