Collaborating Remote Sensing with historical limnological data to map primary productivity at a Eutrophic lake
Pranab J. Baruah
Post-doctoral fellow, Yasuoka Laboratory
Institute of Industrial Science, University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505
Tel: (81)-3-5452-6415
Fax: (81)-3-5452-6410
E-mail: pjbaruah@iis.u-tokyo.ac.jp
Japan
Masayuki Tamura
Deputy Director, Social and Environmental Systems Division
National Institute for Environmental Studies
16-2 Onogawa, 305 0053
Tel : (81)-298-502479
Fax: (81)-298-502572
E-mail: m-tamura@nies.go.jp
Japan
Yoshifumi Yasuoka
Professor, Institute of Industrial Science
University of Tokyo
4-6-1 Komaba
Meguro-ku, Tokyo, 153-8505
Tel: (81)-3-5452-6409
Fax: (81)-3-5452-6411
E-mail: yyasuoka@iis.u-tokyo.ac.jp
Japan
Abstract
Primary productivity is a complex process, especially in shallow eutrophic inland waters,
where there is considerable upwelling and mixing of bottom sediments during winds and considerable
anthropogenic effects from the land-mass surrounding it. For mapping primary productivity by remote
sensing in eutrophic lake therefore often involves costly and laborious sampling of the same
simultaneous to the satellite overpass. This follows relating the lake-surface productivity to satellite
retrieved radiances to develop some empirical algorithm for subsequent mapping. In this paper, a novel
approach is presented where a satisfactory model based on historic limnological data of four parameters
is utilized to map primary productivity at the lake Kasumigaura, Japan. The selected key water quality
parameters, namely chlorophyll-a, suspended sediment, secchi disk depth and water temperature, can
essentially represent the primary productivity and can be estimated from remote sensing imagery. The
developed models can be used for any date of the year to generate satisfactory primary productivity
maps at the lake by feeding the water quality maps of selected parameters as inputs. As the input
variables are fewer, separate models for each month of a year is necessary for better approximation of
the complex process of primary productivity. For the month of January, a neural network is successfully
used to develop the productivity model with a coefficient of correlation (R
2) >0.7 in both training and
validation. Finally, a productivity map of Kasumigaura for 19
th January, 2001 is generated for
demonstration.
Introduction
Inland water bodies constitute less that 1% of the total water
volume of the world and provide us with the much needed
drinking water and water for agriculture. Inland water quality is
directly affected by the conditions of the surrounded land mass
and vice versa. To this very reason and others, inland water
environment is quite complex and dynamic in both spatial as well
as temporal scale. . The productivity in these waters is thus a
very complex process and measuring and estimating the same
provides valuable insight to the present situation. Limnologists
have been using quite a number of components to depict the
inland water quality and for decades, they have been depending
on point samplings to assess the water quality in these waters.
Although traditional point samplings are accurate, they are time-consuming
and, do not provide the necessary spatial overview
required to understand the processes which often vary with wide
range of spatial scales (Harris, 1986). With the advent of remote
sensing, several semi-analytical models have been developed to
estimate primary productivity in oceanic environments from
remotely detected chlorophyll concentrations (and assumed
chlorophyll profile across depth) or sun-stimulated chlorophyll
fluorescence (Esais et al. 1997). However, these algorithms do
not work well in inland waters due to their optical complexity by
virtue of multi-componancy. Moreover, the modern aquatic
sensors are of no use in these waters as they are of
coarser spatial resolution. Mostly, empirical models relating remote sensing reflectance with surface
productivity are developed for mapping primary productivity in inland waters. However, this process is
often costly and laborious as sufficient data samplings concurrent to satellite overpass are necessary for
satisfactory estimation of the same. Moreover, a model developed with this method is often weak in
temporal scale and often can not at all be used to map the productivity at other dates.
In our research, therefore, we developed a methodology whereby the productivity model is based on
already existing pool of data (or data at regular intervals) of some limnological variables which can be
effectively estimated by remote sensing and which essentially can represent the very process of primary
productivity. This way, remote sensing capabilities are integrated with historical pool of limnological data
for meaningful productivity mapping . The inputs to the models are water quality maps of the variables
under consideration for a certain day and output is the primary productivity map of the water body. To
ensure better representation of the complex phenomenon of productivity, neural network has been
used. The historical pool of limnological data used in this study is from samplings spanning several
decades spread over the lake.
