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Using TM data to quantify the contribution of chlorophyll, phytoplankton and fish productivity

Huang Qi-Quan
Remote Sensing Center of Chinese Academy of Fishery Sciences,
Beijing 100039, China


Abstract
In this article, data sampling, spectral measurement, TM image band selection, mosaic, image processing and computer auto classify will be described. An active economic, accurate, and quick method to quantify chlorophyll (CHL) and phytoplankton biomass (PPB) had been designed out by using TM data with computer image processing system.

Methods
  1. Spectrum Measurement and Data Sampling
    The in situ measurement and investigation were carried out during August 24 to 26, 1989. 12 sampling stations were selected by experts who is familiar with lake Taihu. These 12 sampling datas were used as the control data to calculate the CHL and PPB over the whole lake.

    12 spectral reflection and CHL curves were depicted using the in situ data. The spectral band ranges from 400nm to 900nm corresponding to the TM data from bandl to band4.


  2. TM Image acquirement
    Landsat TM data was got from Remote Sensing Ground Station of Chinese Academy of Sciences in may 30, 1989. Two scenes of TM image (path=119, row=38 39) were combined into one scene (6) (Including water body, land, urban and island). By means of task "DF' of image process system the working region of Taihu scene can copy out.


  3. Correlation Analysis and Image Processing
    The 12 groups of sampling data are listed in table 1.

    Station no. CHL (ug/1) PPB (mg/1) Depth (m) Weather
    1 7.5 22.61 1.85 fine
    2 13.8 43.85 3.00 fine
    11 9.9 31.90 2.90 cloudy
    5 2.2 7.42 2.50 fine
    7 2.5 7.70 1.90 fine
    4 3.8 11.97 2.70 fine
    3 3.5 9.09 2.50 fine
    10 3.9 9.15 3.4 cloudy
    9 2.7 6.81 3.00 fine
    8 3.4 11.25 2.70 fine
    6 2.7 6.86 2.20 fine
    12 4.5 19.94 2.60 cloudy


    A.The TM data characters were listed in table 2.

    TM band Band range Colour Spectrum characters
    band 1 0.45-052 blue water quality,depth
    band2 0.52-0.60 green distinguish water and wat.weed
    band3 0.63-0.69 red enhance vegetible, non-vegetible CHL absorbed band
    band4 0.76-0.90 nearinfrared great contrast between plant and water. obvious reflection for water weeds.
    band 5 1.55-1.75 nearinfrared distinguish vegetibles,obvious for soil moisture
    band 6 10.4-12.5 thermalinfrared calculate the surface tem. and eatimate the plant production
    band7 2.08-2.35 midinfrared to map geological structure


    B. Analysis of the correlation coefficient between CHL and TM data
    Generally, the concentration of chlorophyll in large water body is always bellow 10g/1. According to the spectral properties of TM bands, (seetable2.) the correlation between CHL and band6 band7 will not be considered. Because the maximum reflection of CHL is in TM band5 and band2, so we select the TM and2 and band5 to calculate the contribution of CHL in the whole lake with establishing mathematic model of CHL concentrations. Also we know the band1 would be one of the best and to determine water quality i.e. suspended suspended solids in the water [2], so the formula: (band (I-band1)/ band (I))+ (I=2 or 5) is used to modify the effection of the suspended solids for band 2 or band 5 the correlate results between CHL sampling data and TM data are listed in table. 3.

    Table 3.
    band (s) different TM bands combination correlate coef.
    1 B*band 1+C 0.360
    2 B*band2+C 0.639
    2 B*ln (band2) +C 0.641
    3 B*band3 +C 0.455
    4 B*band4+C 0.283
    5 B*band5+C 0.660
    1,2 B*SQRT((band 2-band1)/(band2+ band1)+C 0.851  
    2,3 B*SQRT ((band2-band3)/ band2+ band3))+C - 0.322  
    1,2,5 B*SQRT ((band2- band1)/( band2+ band1))+ band5+C 0.856
    1,2,5 B*Ln((band2- band1)/( band2+ band1)+ band5) +C 0.861


    Table3 shows that the chlorophyll has a good correlation with band 1,2,5,which the regression model is CHL = gain in ((BAND2-BAND1) / BAND2-BAND1)+BAND5) + offset. The CHL contribution map is showed as Fig. 1.


    Fig. 1 Image of CHL distribution,1:750000
    The different color has the different CHL concentration


  4. The calculation of primary fish productivity
    Presently, there are three ways to estimate the fish productivity (1)

    1. To establish the relationship or models between feed biomass or non-biomass factor and fish productivity.


    2. To estimate the fish productivity from feed biomass according to the energy transferring o ecosystem or feed population.


    3. To gain the result of different kinds of fish productivity based on analization of bio-factor and non-biofactor.
Since 1960 there are many regression models between feed biomass and fish productivity, and we will introduce two models which use CHL or phytoplankton biomass to determine fish productivity.

Model (1) log (Yf) = -1.92+1.17*log (CHLS)
Yf ----dried weight of fish production (g/m2)
CHLs --- CHL concentration in summer (mg/1)

Model (2) Yfc = WPB* 0.004
Yfc --- fish production (g/m3, per day)
WPB ---wet weight of phytoplankton biomass (mg/m3)
0.004 --statistic transfer coefficient (80% *20% /40)



According to Model(1). the primary fish productivity (PFP) are computed in table 4.

Table 4
Subarea area(acre) CHLs(ug/l) depth(m) total CHL(kg) PFP(kg)
DONGTAIHU 199962 1.8 2.6 239.7 0.016/3.31
SANSHANHU 186259 7.0 2.6 868.3 0.078/16.41
ZHUSHANHU 45706 3.5 2.6 87.5 0.052/10.93
GONG HU 247953 4.0 2.6 660.5 0.041/8.51
XIAOMEIKO 255951 3.5 2.6 511.5 0.035/7.28
YIXINGTAN 490192 2.9 2.6 950.0 0.028/5.85
TAIHUQV 2174000 3.5 2.6 5067.6 0.035/7.28
TOTAL 364000 ----- ----- 8527.6 -----


According to Model (2) the primary fish productivity ( PFP ) are computed in Table 5.

sub lake Area ( acre ) PPP ( mg/I ) Depth ( m) Total ( PPB Kg. ) PFP ( Kg. )
DONGTAIHU 119962 6.4 2.6 853.4 0.017/3.54
SANSHANHU 186259 30.0 2.6 3727.0 0.081/17.0
ZHUSHANHU 45706 19.0 2.6 579.2 0.051/10.6
GONGHU 247953 15.0 2.6 2480.8 0.040/8.48
XIAOMEIKO 255951 13.5 2.6 2304.7 0.036/7.73
YIXINGTAN 490192 10.5 2.6 3433.1 0.029/60.06
TAIHUQV 21740000 13.5 2.6 19575.8 0.036/7.73
TOTAL 3640000 --- --- 32954.0---


Where:
Depth(m) -----------------the average water depth of 12 sampling stations
CHLs (UG/1) ------------Mean CHL concentration which calculated by image processing system for each sublake
TOTAL CHL (Kg) ------Total CHL quantity of each sub lake [5]
PPP(mg/l) ---------------average concentration of phytoplankton for each sub lake
total PPB (kg) ----------daily production per acre/whole year's production per acre
PFP(kg) ------------------daily production per acre/ whole year's production per acre

Total fish production of the whole Taihu lake (include 7 sub lakes ) in one year calculated as:

Total fish production (TFP) = subarea*FFP (about one year ) = 27520000(kg)

One year's fish production per acre (YFPA) can be calculated as:

YFPA = TFP/total area = 27520000/3640000 = 7.54 (kg)

Results and discussion
  1. Although the two methods described above only involved one time sampled data of CHL and phytoplankton biomass, but we stultified with the two similar results about the primary fish productivity using the two methods. To estimate the concentrations of CHL, phytoplankton biomass and fish productivity by using Landsat TM data and computer image processing system is successful in our research.


  2. During the 1980 to 1981, the primary fish productivity in Taihu lake was 4.55 kilogram/acre (use regular investigation) and it's 7.54kg/acre in 1989. This is because chlorophyll concentration has been changed from 4.84 (ug/1) to 10.5 (ug/1) [7]


  3. It is new method to estimate the natural resources for reproduction and aquaculture in the fresh waterbodies.
References
  • Fresh Water Aquatic Organism, Dalian College of Fish. and Aqui.
  • Li Jilong et al, The Study Of Using Landsat TM Data To Quantify Daihai Chlorophyll And Lake Weed. 90's Asian and Pacific Regional Oceangraphy and Fishery Remote Sensing Symposum.
  • Zhanglin, Huang Qiquan, Monitoring Aquiculture Resources BY Using Remote Sensing Technique in Lake Gerhu. Remote Sensing Center of Chinese Academy of Fishery Sciences.
  • M. Godoy JR; E.M.L.M. Novo, TM/Landsat Data Processing FOR Inland Water Monitoring.
  • O. Jarret, Jr; C. A. Brown; J.W.Cambell etal: Measurement OF Chlorophyll A FlouresceneWith AN Airbone Flourescene
  • J. M. Hill, Inference About Water Quality AND Quality Based Land AND Land USE , R.S. Luisiana Uni.
  • Bao Jianping, The Calculation OF Taihu's Phytoplankton AND Fish Productivity, materials Collection of Taihu Natural resources Investigation.