2001). The workflow of the MDA to generate enhanced land
use and land cover data consists basically of a GIS- and a RS-
part (Fig.1). The task of the remote sensing part is to classify
multitemporal (and multisensoral) satellite imagery and to
provide a classification assessment in terms of data quality.
For the analysis of crop rotations, multitemporal satellite
imagery are of key importance to consider phenological
characteristics (Rohierse and Bareth, 2004). The results are
imported into a GIS environment. Here, the classified data are
combined with additional relevant and available topographical
and/or land cover data. These are usually official data provided
by national surveying and mapping bureaus. The idea is to use
high quality topographic spatial information e.g. about
residential area to improve the land use classification. In the
latter case, all affected land use classes of the remote sensing
analysis will not be considered any more.
Besides official land use data which are available in topographic
information systems like the German official topographic-
cartographical information system called ATKIS
(www.atkis.de) or e.g. the official land use database of China
(http://ngcc.sbsm.gov.cn/english/), numerous land use infor-
mation are stored in various spatial databases. These spatial
biotope/biodiversity databases (e.g. in Germany), spatial
databases of national parks, research projects, water protection
areas etc., can be used for such an approach. Additionally, data
from official statistics like agricultural or land use data have to
be considered (Bareth 2009). The MDA is described in detail by
Bareth (2008).
pu o un
images
oi
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residental, forest, a.m.
e E .URST08 —
Figure 1. Schematic procedure of the MDA
3. CASE STUDIES
3.1. Dairy Farm Region “Wiirttembergisches Allgiu”
The first case study is the intensive dairy farm region of the
"Württembergisches Allgáu" in Southern Germany (Bareth
2000; Bareth et al. 2001). For the MDA, an IRS-1C image (1%
September 1997) was analyzed for land use and was overlaid
with numerous available official GIS data. The most important
GIS data source is the ATKIS, the German topographic vector
database in a scale of 1:25,000. Besides ATKIS, available data
on biotopes, water and nature protection areas as mentioned
above were used for overlay analyses. Hence, the final MDA
land use data contain numerous spatial information besides the
RS derived classification. In Fig. 2, the IRS-1C image is
visualized with the selected topographical GIS vector data. Only
by displaying urban and forest polygons, it is very obvious that
a significant proportion of the image is covered by spatial
knowledge from an additional source.
Figure 2. IRS-1C image with topographical GIS vector data
The generated final MDA land use data serve in a next step as
the base for spatially disaggregating agricultural management.
Here, the N-fertilizer input is presented as an example. The N-
fertilizer input for grassland and arable land is calculated for
each administrative unit (township) by using agricultural
statistics (fertilizer amount, livestock number etc.). The
distribution of the fertilizer amount within a township is based
on the land use data. But most important, some land use classes
in the MDA land use indirectly contain limitations by law for
N-fertilizer input e.g. special water protection zones or special
biotopes. Therefore, following four rules were applied for the
distribution (Bareth 2000):
- extensive (0 - 100 kg N ha'!yr ^):
all land use polygons within the biotope zones and the
water protection zones I and II
- moderate-extensive (101 - 150 kg N ha ‘yr!):
all grassland polygons of township Tettnang
- moderate (151 - 200 kg N ha!yr!):
all grassland polygons of the other townships
- intensive (201 - 250 kg N ha'!yr!):
all arable land polygons
N-Fertilizer in kg ha/yr
[7] extensiv (0 - 100) —— Township Border
[7] 101 - 150 Water
151 - 200
Residential
Forest
0 10 Kilometer
»————
Figure 3. Spatially disaggregated regionalization of N-fertilizer
input for the “Wiirttembergisches Allgäu”
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