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Ž .ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 317–324

Modelling for prediction of global deforestation based on thegrowth of human population

Krishna Pahari ), Shunji MuraiInstitute of Industrial Science, UniÕersity of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo, Japan

Received 18 June 1998; accepted 16 February 1999

Abstract

Deforestation due to ever-increasing activities of the growing human population has been an issue of major concern forthe global environment. It has been especially serious in the last several decades in the developing countries. Apopulation-deforestation model has been developed by the authors to relate the population density with the cumulative forestloss, which is defined and computed as the total forest loss until 1990 since prior to human civilisation. NOAA-AVHRR-basedland cover map and the FAO forest statistics have been used for 1990 land cover. A simulated land cover map, based onclimatic data, is used for computing the natural land cover before the human impacts. With the 1990 land cover map as baseand using the projected population growth, predictions are then made for deforestation until 2025 and 2050 in both spatialand statistical forms. q 1999 Elsevier Science B.V. All rights reserved.

Keywords: forest loss; remote sensing; population density; population-deforestation model; global land cover change; AVHRR

1. Introduction

Global land cover change, particularly from forestto other land cover types due to increased humanactivity, is one of the most important issues in globalchange research. It has been especially remarkable inthe last few decades, which witnessed an increasingrate of deforestation due to pressure caused by thepopulation growth. Since forest is so vital for thesustenance of the ecosystem to which we belong, it

) Corresponding author. Fax: q81-3-5452-6408; E-mail:[email protected]

is becoming increasingly important to make predic-tions about the state of forest in the future underdifferent scenarios to suggest appropriate policymeasures.

Even though significant progress has been madein global change research in recent years, the lack ofa reliable spatial dataset on deforestation continuesto be a major obstacle for modelling global changeŽ .Murai, 1995 . However, it is still possible to analysethe trends of global environment, including defor-estation with the existing satellites, meteorologicaland socio-economic data. In this study, deforestationis addressed relating it to population density andpredictions are made about future deforestation, basedon a projected population growth.

0924-2716r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved.Ž .PII: S0924-2716 99 00032-5

( )K. Pahari, S. Murair ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 317–324318

ŽFig. 1. Historical trend of world population and projection source:.UNPD, 1994 .

There are several factors that seem to be related todeforestation, namely population, GNP, governmentpolicy, land ownership, etc. However, it has beenfound by the authors in the study that population isthe most significant factor in global deforestation.

Human population has grown significantly in thelast few centuries and has been especially alarmingover the last several decades. Fig. 1 shows thehistorical trends of world population and future pro-jections, based on UN medium variant population

Ž .scenario UNPD, 1994 . Obviously, this projection,if correct, is bound to have a tremendous impact onearth resources, including forest.

Fig. 2 shows the general framework of methodol-ogy followed in this study. Details of the varioussteps are discussed in the following sections.

2. Actual and potential natural land cover

It is now possible to monitor the global land coverwith satellite data, particularly NOAA Global Vege-

Ž .tation Index GVI data. Even though developing anaccurate global land cover map is still an ongoingprocess by several researchers and projects, globalland cover maps are now available and provide agood global overview. Fig. 3 presents an updatedversion of the global land cover map from Murai and

Ž .Honda 1991 revised using the AARS 4-min gridŽ .dataset on global land cover AARS, 1997 .

Since land cover change has been occurring overa long time and satellite data for global monitoringhave been available only for the last 15 years, apotential natural land cover map has been developedto simulate the land cover map prior to humanactivities, based on climatic data. Fig. 4 shows thepotential natural land cover map, based on De Mar-

Ž .tonne’s aridity index AI and the classification cate-Žgories introduced in Table 1 based on modified

.criteria by Murai and Honda, 1991 .The AI is defined as:

PiAI s ,Ž . i T q10i

Ž .where P is the total annual precipitation in mmiŽ .observed in cell i and T in degrees Celsius is thei

annual sum of monthly mean temperatures of those

Fig. 2. Framework of the methodology adopted to predict future global deforestation.

( )K. Pahari, S. Murair ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 317–324 319

Ž . Ž .Fig. 3. Global land cover map for 1990 from NOAA GVI data, based on Murai and Honda 1991 and AARS 1997 .

months with monthly mean temperature greater than0, divided by 12, for cell i.

Global rainfall and monthly mean temperatureŽdata interpolated into a surface grid of 10 min

.resolution for the last 30 years were obtained fromthe University of Tokyo.

Based on the above analysis, Table 2 shows theresults of potential natural land cover and actual land

Fig. 4. Potential natural land cover map, based on climatic data.


Table 1Ž . Ž .Aridity index AI and land cover types Murai and Honda, 1991

Land Cover Types AI

Desert F5Semi-desert 5-AIF10Grassland 10-AIF20Forest )20

Ž .cover for 1990 on a global level. The table showsthat humans deforested 15.3% of the potential forestarea and increased the potential area covered bydeserts and semi-deserts by about 14.8%.

3. Population-deforestation model

Out of several correlation analyses studied by theŽauthors such as GNP and deforestation, several

.combinations of population and deforestation , it wasfound that the correlation between the logarithm ofpopulation density and the cumulative forest losscomputed from potential natural land cover and ac-tual land cover was most significant. More specifi-cally, the correlation of GNP per capita with forestloss was very low in all tested areas.

Due to the unavailability of detailed historicaldata about the global forests over a fairly long periodto conduct trend analysis and to make projections,cross-country data obtained by grouping countrieswith similar ecoclimatic zones and similar stages ofeconomic development have been used to developthe model introduced in this paper.

Although Fig. 3 provides a good global overviewof forest and other land covers and fits globally withthe FAO forest statistics, the authors concluded thatFAO’s statistics led to more reliable estimates of theforest cover for individual countries.

Table 2Ž .Percentage of potential and actual 1990 global land cover

Land cover Potential Actual Change fromtype area area original

Forest 48.46 33.20 y15.26Grassland 34.27 34.73 q0.46Semi-desert 8.36 15.79 q7.43Desert 8.91 16.28 q7.37

Table 3ŽCumulative forest loss in selected countries forest cover in 1990,

.based on FAO, 1997

Country Potential Forest cover Cumulative forestŽ . Žforest in 1990 loss % column

Ž . Ž .% % 2ycolumn 3r.column 2

Brazil 97.54 66.68 31.64Peru 91.95 53.63 41.67Bolivia 92.39 47.22 48.89Ghana 100.00 42.23 57.77Cameroon 97.88 43.50 55.56Zimbabwe 74.84 23.16 69.05Bangladesh 100.00 8.10 91.90Thailand 99.53 25.99 73.89Malaysia 97.27 53.18 45.33India 82.28 21.85 73.44Nepal 83.81 37.25 55.55Former USSR 41.87 37.96 9.34France 99.28 25.87 73.94UK 98.98 9.63 90.27

In this study, the cumulative forest loss for eachcountry was computed as the total forest loss ob-

Ž .served from the potential land cover map Fig. 4and the current forest cover for 1990, based on FAOforest statistics.

A regression analysis was conducted using thelogarithm of population density as independent vari-able and cumulative forest loss as dependent vari-able. The countries were grouped into regions, basedon similar ecoclimatic zones and level of socio-eco-nomic development.

Table 3 presents the cumulative forest loss forselected countries. Table 4 gives a summary of thecorrelation index between natural logarithm of popu-

Table 4Correlation between population density and cumulative forest lossfor different regions

2Region Regression function R

Ž .Tropical Asia 16.042 ln x y19.56 0.638Ž .Tropical Africa 15.206 ln x q7.8446 0.717Ž .Sahelian Africa 16.872 ln x q12.305 0.638Ž .Tropical Latin America 16.896 ln x y7.020 0.672Ž .Central America and Mexico 21.637 ln x y29.643 0.824Ž .Europe 14.719 ln x q0.728 0.523


lation density and cumulative forest loss for variousregions. Fig. 5 shows the scatterplots for population

Ž . Ž .density logarithm and cumulative or total forestloss for various regions.

Ž 2 . Ž . Ž . Ž .Fig. 5. Scatterplot between population density personsrkm , logarithm and cumulative forest loss % for a Tropical Asia; b TropicalŽ . Ž . Ž . Ž .Africa; c Sahelian Africa; d Tropical Latin America; e Central America and Mexico; and f Europe.


Ž . Ž 2 .Fig. 6. a Historical trends of population density personsrkmŽ . Žand forest cover % of total land in Thailand forest cover,

mainly based on Mather, 1990 and population density, based on. Ž .Statistical Year Books of Thailand . b Analysis of historical

Žtrends of population density and deforestation in Thailand 1949–.1991 .

In order to see that such linkages between popula-tion density and forest loss hold true for time seriesanalysis for a given country, this method has beentested using time series data of population densityand forest cover of Thailand. Fig. 6a and b show theresults of such analysis for Thailand linking popula-tion density and the total forest loss and it shows that

Ž 2 .there indeed exists a high correlation R s0.9736between logarithm of population density and cumula-tive forest loss.

4. Predictions for deforestation

Having established the population-deforestationmodel presented above, predictions have been madefor the future state of deforestation. Deforestation foryears 2025 and 2050 was estimated for each countryby using the population-deforestation model and theprojected population density, based on UN medium

Žvariant long-range population projections UN,.1997 , and then the annual deforestation rates d andi

the forest loss L since 1990 were calculated byi

using the 1990 forest cover and the predicted forestcovers for 2025 and 2050 as:

LŽ1990 – 2025.

s100= forest 1990y forest 2025 rforest 1990,Ž .

and

dŽ1990 – 2025.

1r35s100= 1y forest 2025rforest 1990 ,Ž .� 4and similarly for 2050. Table 5 shows the results ofsuch predictions for different regions.

It can be seen from Table 5 that the deforestationscenario is most serious for Tropical Africa, fol-lowed by Sahelian Africa, central America, TropicalAsia and Tropical Latin America. The African regionis also the area where the population is projected toincrease most rapidly in the coming years.

The predicted deforestation map for any year, say2025, is then simulated using the following steps.

Ž .1 The land cover map for 1990, as presented inFig. 3, is used as the starting point.

Table 5Scenario of deforestation from 1990 to 2025 and from 2025 to 2050

Ž . Ž . Ž .Region Forest coverage 1990 % Predicted annual deforestation rate % Forest loss since 1990 %

1990–2025 2025–2050 2025 2050

Tropical Asia 34.81 0.55 0.30 17.44 23.39Tropical Africa 36.94 1.15 0.68 33.27 43.67Sahelian Africa 13.52 1.00 0.43 29.62 36.87Tropical Latin America 61.24 0.37 0.29 12.03 18.29Central America 31.58 0.65 0.39 20.50 27.87Europe 36.78 0.01 0.00 0.26 0.26World 33.20 0.24 0.11 8.13 10.54


Fig. 7. Map showing prediction of deforestation from 1990 to 2025.

Ž .2 Based on the gridded population density mapŽ .of the world for 1994 CIESIN, 1996 , and using the

UN medium variant population projections for eachcountry, a map is prepared for the predicted popula-

Žtion density for the year under consideration here,.2025 and 2050 .

Ž .3 From the map of 1990 land cover, the cellsŽ .with the highest population density from step 2 for

each country are then assigned a new land coverclass from forest to non-forest such that the totalnumber of cells so converted is equal to the totalpredicted forest loss for that country.

Fig. 7 displays the predicted map of deforestationfor the period 1990–2025, generated by using theabove procedure.

5. Discussion of results

If we compare the predicted rates of deforestationin the next five decades, it appears that deforestationis going to be a significant problem especially in thedeveloping countries of the tropical region. How-ever, the speed of deforestation is likely to be lesscompared to the peak period of 1980s. This con-forms with the trends of population growth, as there

are signs that the population growth is now actuallyslowing in many parts of the world, even though it isstill going to be very significant. One exception tothis is Africa, where the population growth is stillpicking up and is projected to slow down only afterthe next few decades. That is why the deforestationrate in Tropical Africa is likely to be most serious inthe next several decades.

While there is some hope in the sense that the rateof deforestation is expected to slow down in mostpart of the world, however, if we consider the avail-ability of forest resources for the increasing popula-tion, the situation looks very serious. Table 6 shows

Table 6Projected forest area per capita

Ž .Region Forest area per capita ha

1990 2025 2050

Tropical Asia 0.19 0.09 0.08Tropical Africa 1.35 0.36 0.22Tropical South America 3.46 1.94 1.56Central AmericarMexico 0.58 0.29 0.22Tropical average 0.72 0.33 0.25Sahelian Africa 0.74 0.20 0.13Europe 1.24 1.24 1.24World average 0.82 0.48 0.41


the projected forest area per capita for differentregions until 2050. It is a matter of further investiga-tions as to what are the requirements of forest areaper person for healthy and environmentally soundliving conditions. However, one thing that is veryclear is that the availability of forest per person willdecrease very drastically, especially in the develop-ing countries, which is likely to pose a major chal-lenge for human well-being.

6. Conclusions

The authors have established a model for linkingpopulation density with the deforestation, to predictthe future state of deforestation. It is seen that eventhough the rate of deforestation is somewhat decreas-ing, deforestation will continue to be a significantproblem in the next several decades, especially in thedeveloping countries of the tropical region. Africa islikely to have the most rapid deforestation followedby Tropical Central America, Tropical Asia andTropical Latin America. The situation looks veryserious in terms of forest resources available percapita in developing countries.

Although forest loss is caused by various factorsincluding the market of forestry exploitation, andthus forest loss in certain regions may not be directlyrelated to the population increase, the above analysisof the global trend as a whole, shows that in general,deforestation is highly correlated with the logarithmof population density.

Further work on global deforestation modelling,using more accurate datasets as they become avail-

able and possibly incorporating other factors such asGNP into the model, is being considered for futureresearch. Another topic of future works is the estab-

Ž .lishment of some thresholds refraining destructionto minimise the projected tendency of forest loss.The authors are also working on other aspects ofglobal environment related to global deforestation,such as carbon fixation, primary productivity, carry-ing capacity, etc.

References

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CIESIN, 1996. Gridded Population Density of the World. Consor-tium for International Earth Science Information Network, MI,USA.

FAO, 1997. State of the World’s Forests 1997, Food and Agricul-ture Organisation of the United Nations, Rome.

Mather, A.S., 1990. Global Forest Resources, Belhaven Press,London, 25 pp.

Murai, S., 1995. Development of global eco-engineering usingremote sensing and geographic information systems. In: Mu-

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