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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
(1:200 000) digital soil dataset (soil dataset B) was provided by
the China Wetland Scientific Database (CWSD, 2011). This
data was digitized according to maps which were also generated
from the SNSGS. In contrast to the soil dataset A, the soil
properties are not so detailed, and the spatial resolution is not as
large as expected (Table 1).
For our study, soil samples collected in the area of the Qixing
Farm are used. From 2005 to 2007, more than 6000 soil samples
were taken by the staff of the Qixing Farm in a depth of 0 to
20 cm distributed over the area of the Qixing Farm. This data
and soil samples taken during our field campaigns will be used
as a reference dataset.
23 Climate Information System (CIS)
The CIS provides the climate parameters for the agro-ecosystem
modeling. Climate data should be available in daily or hourly
temporal resolution, according to the model input data
requirements. Weather maps with detailed information such as
air temperature, precipitation, wind speed etc. are needed.
In this study, data of seven weather stations in the SJP was
downloaded from the China Meteorological Data Sharing
Service System (CMDSSS, 2011). All datasets dating back to
1951 have a daily resolution and include temperature,
precipitation, air pressure, wind speed and directions, air
moisture, etc.. Additionally, two books (written communication:
Agricultural Bureau of Heilongjiang Nongken, 2003;
Jiansanjiang Administrative Farm, 1998) were provided by the
local weather information bureau. Here, daily weather
information is provided from 1991 to 2000 for 82 farms in
Heilongjiang Province, 47 of them are within the SJP. Both
daily and hourly data in digital format was collected from the
Jiansanjiang weather station (located in the northern part of the
Qixing Farm) in 2011. The daily data was recorded from 2000
to 2010, and hourly data from an automatic weather station
covers 2010 to 2011. In the last years, more automatic weather
stations were installed which provide hourly data.
Climate maps will be generated using GIS interpolation
methods within the AEIS. The number of weather station sites
must be adequate enough to provide detailed information.
Automatic weather stations can provide even minutely data and
more accurate information. However, power cuts, occurring
frequently in the intensive farming season, can significantly
impair the data quality.
24 Land Use Information System (LUIS)
Land use provides a strong integrative tool for revealing agro-
ecosystem patterns. The LUIS in the AEIS focuses on the arable
land use. Detailed land use maps from different periods are
required to reveal the farming patterns and investigate crop land
changes. Ideally, the land use maps should not only give
information about the major crops, but should differentiate
between the different crop varieties and should contain crop
rotations (Waldhoff et al., 2011).
In the present study, the 1:100 000 land use vector datasets for
the years 1980s, 1995 and 2000 were purchased from the Data
Center for Resources and Environmental Sciences, Chinese
Academy of Sciences (RESDC, 2011). These land use maps are
generated from satellite images provided by Landsat TM
(Thematic Mapper) images from the 1980s, 1995/1996 and
1999/2000. The maps only give information about forest, cities,
Waters, grassland and arable land etc. on the county level, but
not about crop type and variety which are needed for detailed
agro-ecosystem modeling.
To solve the problem of lacking spatially disaggregated crop
type data, remote sensing data and analyzes will be used and
carried out. Similar approaches as described by Zhang et al.
(2012) and Waldhoff et al. (2012) will be applied.
2.5 Agricultural Management Information System
Farm management data such as sowing/transplanting/fertilizing
date, amount of nitrogen fertilizer input, irrigation management
(especially for rice), plant protection management, harvest time,
or special regime of management are provided by the AMIS.
Information is needed both on county and farm level, in the
PCTV and NAFV system.
In this study, original statistical data in the PCTV system on
county level from 1950 to 2004 was provided by the Northeast
Institute of Geography and Agroecology, Chinese Academy of
Science, China Black Soil Ecology Database (CBSED, 2011).
In these data sets, information on county level such as sowing
area, chemical fertilizer input, yield of main crops etc. is given.
For 2005 to 2011, the statistical data for the SJP is found in the
Heilongjiang Statistical Yearbooks (Heilongjiang Statistical
Bureau and Heilongjiang survey team from the National
Bureau, 2006-2011), but some necessary information on county
level is not given. Farmers’ surveys about management data
have taken place every year since our working group activities
in the study area started in 2005. More detailed statistical data
covering 2005 to 2011 on county level in the PCTV system is
needed and statistical data on the farm level in the NAFV
system has to be organized. For the site-specific study area of
Qixing Farm, farmers’ surveys will be carried out during our
field campaigns in the future.
For the AEIS, more digital maps will be generated from the
available statistical data. Local bureaus such as the Heilongjiang
Nongken Statistical Information Bureau will be contacted.
2.6 Hydrological Information System
Hydrological characteristics are essential for the agricultural
production, especially in a rice dominated farming area such as
the SJP. An HIS provides the information about the water
balance cycle in the region and its basic processes, such as
precipitation, interception, evapotranspiration, water flow in
soils, ground water, surface runoff, interflow, ground flow,
stream flow etc.. Long-term hydrological data can be acquired
according to the records of the local water conservancy bureaus.
Site-specific hydrological information monitoring campaigns
and remote sensing methods can also be applied (Bareth et al.,
2009). For the SJP, until now very little efforts were spent on
such data acquisition. Consequently, the HIS for the SJP is a
task for the near future.
3. RESULTS
First results of integrating various spatial and statistical datasets
in the AEIS for the SJP are presented here. The value of
visualizing multitemporal statistical information on agriculture
lies in giving important knowledge on the land use history and
on the land use change in the study region. Both are important
to better understand the matter fluxes in the given context.
