A study on the relationships between man and biological diversity using Remote Sensing data

A study on the relationships between man and biological diversity using Remote Sensing data

Shintaro Goto, Kengo Asakura
Mitsubishi Research Institute Inc.
2-3-6 Otemachi Chiyoda-ku. Tokyo 100, Japan

Shunji Murai,Yoahiaki Honda
Institute of Industrial Science University of Tokyo
7-22 Roppongi, Minato -ku, Tokyo 106, Japan

Abstract
The goal of this study is to make the basic data for discussing about the relationships between population problem, food problem, and green house effect.

For this purpose, by using the vegetation Map, we select the potentially arable area. And we try to consider the relationships between the present vegetation diversity and human activity, such as food consumption.

Introduction
The senses by FA0 and UNEP reported that the tropical forest was decreasing by 11,300,000 ha per year between 1981 and 1985. The cause of that is respected to be the cultivation by human. It is pointed out that the decrease of tropical forest is cased directly by the excess of picking charcoal, unsuitable deforestation for business and forest fire.

The background of these causes, there exists a poverty and abrupt population increase, and they are interlocking each other.

In this study, we deal with the relationships between biological diversity and social information, such as population, agriculture productivity and food consumption, by comparing the potentially of agricultural productivity derived form remote sensing data social information.

Data and Methodology

Data

The data used in this study is largely diverted into tow kinds, such as natural information to make Vegetation Map and social information to consider the relationships between vegetation and human activities.

These data, which has been used in this study, are as follows:
1. Natural Information
  1. Weekly GVI (Global Vegetation Index) data from January 1983 to December 1987.
  2. Monthly averaged value of temperature, rainfall and moisture from January 1983 to December 1087, provided the Japanese Meteological agency, detected at 2344 observation stations all over the world.
  3. Bathymetric data
2. Social Information
  1. Population data quoted from world population prospects.
  2. Agricultural productivity data quoted from production year book.
  3. Food consumption data quoted from food balance sheet 1971-81 Average.

Methodology

fig 1 shows the process flow for calculating the potentially of agricultural productivity and supportable population from the GVI data.

Fig. 1 Process Flow

Tab 1. Energy of crops per 100g

Crops	Calorie (kcal)
Paddy rice	3 5 1
Wheat	3 3 5
Barley	3 3 9
Maize	3 5 0
Sorghum	3 3 6
Soyabean	4 2 7 . 5
Cassava	3 4 6
Millet	3 0 7
Potatoes	7 7
Rye	3 3 3
Sugar cane	5 4

In this flow, the method how to make Global Vegetation Map is depends on the method Honda and Murai.

The details are as follows:

Pick out data from GVI

Necessary NVI (Normalized vegetation Index) values at 2344 weather observation points 9world information), are being picked out from the GVi data for 5 year period (1983-1987). The NVI values can be compared with the weather data for the same points.
Choose stable points of monthly vegetation change.

The stabilities of the monthly vegetation changes are computed by using eq. 1 below at each observation point.

--------------------------(1)

in which SMVC : the stability of the monthly vegetation change,

NVIym : Maximum NVI (year : y, Month : m),
: Average NVI for 5 yeas (1983-1987)
Group the observation point

The grouping criteria depends on the maximum of the NVi data and the total the total number foi months at each NVI level at each observation point.
Output the vegetation map.

By Typical patterns of monthly vegetation change and comparing with Koppen's climatological map, the points are classified into tropical rainforest, ever green forest, tundra, grassland, semidesert, alpine desert.
Calculate the Potentially Arable Area (PAA ha)

The part of grassland in the vegetation map had monthly vegetation change patterns. It is considered that these patterns are due to the change of temperature and precipitation. We define that these parts are the potentially arable land.
Calculate the amount of crops per area harvest.

We choose eleven kinds of corps in the order of amount and the production and are harvest quoted from production yearbook.

As the energy included in each crops is different each other, the amount of crops is converted into energy using the coefficients in Tab.1

The amount of crops per area harvest is calculated (ACPAH Cal/ha) by the eq. (2) by each country.

-----------------------(2)

In which

-----------------------(3)

AP : Agricultural productivity (cal)
PAC (X) : Production amount of crops X (g)
CCAE (X) : Coefficient for conversion from Caloly to Energy (Cal/g)
AH X() : Area Harvest (ha)
Calculate the amount of food consultation per person

The amount of food consumption is quoted from food balance sheet (4) and the amount of food consumption per person is calculated (AFCP cal/person) by the ex. (4) by each country.

AFCP =AFC/POP ------------------------(4)

In which AFC = ----------------------(5)

POP : Population (Person)
AIC : Amount of imported corps X (g)
AEC : Amount of Exported crops x (g)
Calculate the potentially of Agricultural productivity (PAP cal) and supportable population (SP person).

The parameters above are calculated by the eq. (6) and (7) respectively.

PP = PAA x ACPAH -----------------------(6)

SP = PAP/AFCP-----------------------(7)

Result
fig 2 shows the vegetation map and Fig 3. shows the potential arable land chosen from fig. 3

In fig. 2 the high latitudes, the classification of tropical forest region and evergreen region are confounded. And the forest region in tundra is classified into forest and grassland, but the further research is needful, because the grassland in tundra is recognized as potentially arable land.

Fig 2 Vegetation Map

Fig 3 Distribution of Potentially Arable Land

After these consideration, we will show the potentially of agricultural productivity and supportable population in the conference.

Conclusion
The Results of this study leas to the conclusions.

By linking the natural information from vegetation map to the social information, the potentially of agricultural productivity is derived, so it becomes possible to consider the relationships between the regional food balance and the human activities.

The further research will pay attention to the classification of grassland and to the categories of choosing potentially arable area.

References

Yoshiaki Honda and Shunji Murai : Vegetation Mapping using global Vegetation Index and Weather Data. Proc. On the 10th Asian conference on remote sensing P.A -2-4-1~P. A-2-4-6, 1989
Untied Nations : World Population Prospects 1988
FAO : Production Yearbook, Vol 4 1987
FAO : Food Balance Sheet 1971-81 average 1985.