Estimation of NPP based agricultural production For Asian countries using Remote Sensing data and GIS

Estimation of NPP based agricultural production For Asian countries using Remote Sensing data and GIS

Shiro Ochi and Ryosuke Shibasaki
Institute of Industriial Science , Univ. of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505
Tel : (81)-3-5452-6415 Fax: (81)-35452-6408
Email: ochi@cc.iis.u-tokyo.ac.jp ,shiba@skl.iis.u-tokyo.ac.jp
Japan

Abstract
The land use / land cover has been changed for many countries of Asia In last a few decades caused by the population pressure . Generally speaking, the agricultural land increased and forested land decreased. Then, the agricultural production has been increased for many years in the region. However ,there is some doubt that the region can sustain or improve the production In future . The productivity may vary depending on the environment of the land .In this study, the estimation of agricultural production reflecting the current land use/land cover is made .

The agricultural production is considered to be a part of NPP(Net Primary Production) on the agricultural land . The NPP can be estimated using PAR (Photo synthetically Active Radiation ) and NDVI, and both can be seen in the land use /land cover map. The estimation of NPP on the agricultural land of Asian countries is made . Moreover , by Integrating the result of the agricultural NPP with the statistics, the conversion efficiency of agricultural production from agricultural NPP is made. The conversion efficiency shows the status agriculture for the country.

1.Introduction
According to the Announcement, world population reaches 6 billion in October 1999.Monitoring, estimating and forecasting agricultural production are quite important for the management of world / regional or local food demand and supply balance for social security. A method to monitor and estimate agricultural production in coarse however broad frequency scale is introduced in this study.

2.Data Used
Following data were prepared and used in this study.

NDVI Data
NDVI data were used to calculate Net Primary Production in combination with Photosynthesis Active Radiation data. Monthly. NDVI data for January 1982 to December 1993 were composed from 10days composite data set of NQAA/ NASA Pathfinder AVHRR Land Data Set.

Photosynthesis Active Radiation Data
Monthly Photosynthesis Active Radiation(PAR) data for January 1979 to December 1989 were provided ,and averaged monthly PAR data using these data were prepared in order to calculate Net Primary production of 1990 to 1993 in combination with NDVI data .

Land Cover Data
Land cover data were retrieved from USGS Global Land Cover Characteristics Data Base < http:// edcwww.cr.usgs.gov/landdaac.glcc/_ na.html> .There are 6 maps in different schemes in the data base, and IGBP Legend map was used in this study.

Agricultural Production Data (Statistics)
Agricultural production data were retrieved from FAOSTAT agricultural Data . In this study, "cereal production"was used to represent agricultural production.

3.Methodology

3.1 Estimation of Net Primary productivity
Monthly Net primary Productivity (NPP) can be estimated using NDVI and PAR data by "production efficiency approach" proposed by Go ward ( 1992)and Ruimy (1994).

NPP = eò APARdt ...........(*)
NPP: [gdm/m2time],
e:efficiency[g/MJ],
APAR: Absorbed PAR [MJ/ m2]

f APAR = -0.08+ 1.075 NDVI ......(**)
The annual total NPP can be estimated as follows:

3.2 Extraction of Net Primary Production from crop lands
After calculate NPP on whole land , Net Primary Production in cropland is extrated by overlaying USGS/IGBP legend land cover map.

3.3 Calculation of NPP - Crop conversion ratio
By adding NPP in crop lands for countery base , the relationship between NPP in croplands and cereal production is analyzed. The conversion ratio from NPP in croplands to cereal production is calculated for each country.

4. Results
Fig4.1 shows the relationship between global land net primary production and world crop production . The primary production has correlation with the crop production .

Figure 4.1: Relationship between world crop production and NPP on world lands.
Table 4.1 shows the calculated NPP for land cover category base . The values are similar with the exiting values even the categories definition is different.

Table4.1 Calculated NPP based on land cover legends

	Area	NPP per area	Total NPPP
	Mega km²	g/m²/year	giga ton/year
Evergreen Needleleaf forest	6	996	6.3
Evergreen broadleaf forest	12	2170	25.9
Deciduous Needleleaf forest	2	709	1.4
Deciduous broadleaf forest	3	1380	4.4
Mixed forest	6	1078	6.7
Closed Shrub lands	3	834	2.1
Open Shrublands	18	262	4.7
Woody Savannas	10	1306	13.2
Savannas	9	1199	11.1
Grasslands	11	629	6.9
Permanent wetlands	1	543	0.7
Crop lands	14	1189	16.5
Urban and Build-up	0	1175	0.3
Crop /Natural Veg.Mosaic	14	1343	18.6
Snow and lce	2	20	0.0
Barren or Sparsely Vegetated	18	37	0.7
Total	129	924	1194

Table 4.2 shows the relationship between NPP in croplands and cereal production , and calculated NPP - Cereal conversion ratio for some Asian countries. The conversion ratios are considered the status of efficiency or intensity of agricultural activities for the countries.

Table4.2 Cereal Production and NPP-Cereal conversion ratio

	(1)Harvested Area(cereal)	(2)Cereal Production ton	(3)Yield (cereal) ton/	(4)Croplands area	(5)NPP on croplands ton	(6)NPP on croplands/area ton/	(7)NPPCereal conversion ratio %
Japan	24715	1449100	585	43422	49906	1149	51
China	935556	404387700	432	1898806	1681757	886	49
S.Korea	14410	8434015	585	16510	21598	1308	45
N.Korea	16550	5965700	360	27172	33917	1248	29
Bangladesh	111406	27746670	249	93661	105720	1129	22
Iran	94681	13683860	14	57956	38736	668	22
Pakistan	118641	20957200	177	239918	204941	854	21
Vietnam	64635	19901100	308	112646	172252	1529	20
Myammer	52213	14423780	276	140561	194162	1381	20
Indonesia	136605	51912780	380	457408	908900	1987	19
India	1025359	193919300	189	1760687	1759368	999	19
Sri Lanka	8698	2578860	297	25514	52919	2074	14
Thailand	105369	21169690	201	308007	465690	1512	13
Malaysia	7006	1995000	285	121919	269133	2207	13
Mongolia	6559	719638	110	8406	7328	872	13
Loas	6870	1558061	227	23596	49587	2101	11
Philippines	71384	14739140	206	125502	252936	2015	10
Cambodia	17850	2588000	145	86075	159224	1850	8
USA	646592	342512000	530	1142858	1396695	1222	43

Reference:

Goward , S.N. and Huemmrich, K.F.(1992), vegetation Canopy PAR Absorptance and the Normalized Difference Vegetation Index: An Assessment Using the SAIL Model, Remote Sensing of Environment, 39,pp119-140
Ruimy, A., Saugier , B. and Dedien, G.(1994), Methodology for the estimation of terrestrial net primary production from remote sensed data, Journal of Geophysical Research, 99,pp5266