METHODOLOGICAL GUIDELINE TO PRODUCE

A LAND SUITABILITY MAP FOR PALM OIL IN

PAPUA NEW GUINEA

By:

Giancarlo Raschio

Freddie Alei

Frank Alkam

November 2016

1 Table of Contents

1. Introduction ................................................................................................... 3

2 Methodology .................................................................................................. 3 Step 1: Data identification ............................................................................................ 3 2.1 Step 2: Inputs preparation ................................................................................... 7 Step 3: Data conversion from vector to raster ............................................................... 8 Step 4: Reclassification for critical variables ................................................................ 10 Step 5: Overlay analysis ............................................................................................. 11

Analysis and interpretation of results .................................................................. 13

1. Introduction

Land suitability analysis is a useful tool for land use planning and sustainable

development of agricultural expansion. As part of the consultancy for “Agricultural

Mapping Assessment in Papua New Guinea”, our team developed a land-suitability

model based on international best practices.

To test the application of this land-suitability model, our team conducted an

assessment to select three (3) pilot provinces in PNG. The pilot provinces where the

model was tested were East Sepik, West Sepik and Madang provinces.

The results from a suitability map are a key input to develop a future deforestation

model in PNG.

2 Methodology

We followed five steps and used Geographic Information System (GIS) software

(ArcGIS 10.1) to generate a suitability map. Step 1 involved the identification of

biophysical variables and data collection on suitability thresholds for each variable

(Table 1). Step 2 involved preparing the variables and workspace to generate to make

sure all were set up in the same coordinate system and reference projection. Step 3

required data conversion– polygon to raster data. Step 4 involved reclassifying all

rasters to a common suitability scale by using the suitability thresholds identified

from Step 1. Finally, Step 5 was about performing an overlay analysis with all the

selected variables to create a suitability map, reclassification of the suitability results

and conversion to a desired suitability scale and projection system.

Step 1: Data identification

The first step was the identification of the assessment variables that define the

suitability of the land for palm oil cultivation in a given area. We identified 9

variables based on Shearman and Bryan (2008), Pirker and Mosnier (2015) as suitable

to grow palm oil as shown in table 1. For each variable we identified value thresholds

that determine suitability classes for oil palm growth and given a threshold of 1 – 6

for its land suitability as indicated in table 2.

Table 1: The list of data used to create the oil palm suitability analysis

Datasets Description

PNG Provincial Administrative Boundaries PNG Provincial Boundaries

2008 Topographic

Slope Shapefile representing slope

degrees

Altitude Shapefile representing

elevation

Climatic

Rainfall Shapefile representing average

rainfall data

Mean Temperature Min Shapefile representing average

minimum temperature

Drainage Shapefile representing soil

drainage

Soil

Depth Shapefile representing soil

depth

Texture Shapefile representing soil

texture

Erodibility Shapefile representing soil

erodibility

Shapefile representing

inundated areas

Inundation

Source: Shearman and Bryan (2008)

The mentioned layers are the criteria addressing. Different and/or additional criteria

can be considered and generally this process can be done using the participatory

approach by a group of experts from various disciplines.

The combination procedure of the layers follows the conventional scheme for

GIS based MCDA (Malczewski, 1996). It involves three main steps:

1) The first step is to assign criterions and inserted into the model builder

(ArcMap 10.1) to generate the map. Factors are given numerical values that

indicate if their presence in terms of not suitable for the growth of palm oil or

are favourable to the growth of palm oil. In this process, the primary objective

is to investigate the allocation of land to suit specific purpose based on a

variety of attributes that the selected areas have. By applying this process, it

makes it possible to generate compromise alternatives and rankings of

alternatives according to their performance to their attractiveness.

2) The second step, we reclassify the datasets. Reclassify each dataset to a

common scale (for example, 1 to 6), giving higher values to more suitable

attributes.

3) The final step is the Weight and combine datasets. We ran the analysis to

weight the datasets using Arcmap model builder 10.1 to see which datasets

have more influence in the suitability model if necessary, then we combined

them (attributes) to create the suitability map of palm oil.

Table 2: Palm Oil suitability classes threshold variables

Suitability Classes (from perfectly suitable =6 to not suitable = 1

Variables 6 5 4 3 2 1

Slope in

degrees o

<5 5-10 10-15 15-20 20-25 >25

Elevation a.s.l <500 500-900 900-1100 1100-1300 1300-1500 >1500

Rainfall mm 2900-3100 3100-3300 3300-3600 3600-3800 3800-4100 >4100

Temperature

(Maximum) oC

24-31 21-24 20-21 19-20 18-19 <18

Temperature

(Minimum) oC

>21 21 – 20 19 – 17 16 – 15 <15

Perfectly Suitable

6

5

4

3

2

Not Suitable

1

Drainage Well Drained

Imperfectly Drained

Poorly to very

poorly drained

Waterlogged (swampy)

All others (Water

Body)

Depth >100

Deep

50 - 100

Moderately deep

25 - 50

Moderately shallow

<15 - 30

Shallow

All others (Water

Body)

Texture Sandy loam, loam,

silt loam, silt, silty

clay loam, sandy clay

loam, clay loam,

organic mud

clay, silty clay, sandy

clay

Heavy clay, silty heavy clay,

sandy heavy clay, clay, sandy

clay

Sandy, Loamy Sands,

gravels

Peats/Rock – Mucks,

peats 'something' peat

on peaty mucks

Erodibility Soils with high to

very high organic

matter content and

moderate to rapid

permeabilities.

Granular to fine

crumby surface

horizons. Some

lowland Andepts may

have moderate very

fine sand and silt

contents

Except for sandy

Entisols, these soils

have moderate

organic matter

content and moderate

permeabilities. The

sandy Entisols have

generally low organic

matter content and

are rapidly permeable

and structure less

Generally slowly permeable

soils with moderate organic

matter content; the alluvial

Entisols have low to moderate

organic matter content, are

massive and may have

moderate very fine sand and

silt content

Vertisols: very slowly

permeable, often subject to

surface scaling and have

prismatic or coarse blocky

structures, but moderate

organic matter content.

Ultisols and Alfisols:

generally relatively low

organic matter content and

relatively high very fine

sand and silt content. Poorly

structured topsoils.

Others – Water bodies,

lake

Source: Own Elaboration based on Shearman and Bryan (2008), Pirker and Mosnier (2015)

2.1 Step 2: Inputs preparation

After the identification of all the variables and their threshold, the second step is to prepare

the inputs into the ArcGIS model builder.

For each variable add a new field. In this new field we have assigned the suitability scores

depending on the threshold values of the variable.

The Model Builder can be accessed from the Geoprocessing menu or from the standard

toolbar in ArcMap v10.1. Once the model builder is open, the variables were added by using

the add data and the tools can be searched for and added to the variables in the model (Fig.

1), the next approach was to set the model environment (Fig. 2).

Figure 1: Adding variables to the model builder in Arcmap 10.1

Figure 2: Environment Settings in Model Properties. Only Workspace, Processing Extent and

Raster Analysis were selected.

Step 3: Data conversion from vector to raster

In step 3, check to see that all the shapefiles are in the same coordinate system and reference

projections. To do this go to “Data management tools > Projections and transformations >

Features > Project”. If you want to transform more than one vector file at the same time you

can select “Batch Project” instead of “Project”.

Figure 3: From the project tool, select Output Coordinate System and use WGS 1984 UTM

Zone 54S. Set this projection to all other features/variables as well. (Note: PNG is situated in

Zone 54 in the extreme west towards Indonesian border and to Zone 56 extreme east towards

Solomon Islands borders).

Figure 4: Example of a model in Step 3. After the variables were added, they were re-projected

to WGS 1984 UTM Zone 54S /55S and 56S.

Once all files are in the same coordinate system and reference projection, convert all vector

files into raster files. To do this go to “Conversion tools > To Raster > Polygon to raster”.

This tools will ask to indicate a value field for the raster; this value should be the field with

the suitability scores created in Step 2.

Figure 1: Example of a model in Step 3

Step 4: Reclassification for critical variables

For those raster files representing critical variables (in our case altitude, slope, rainfall, and

minimum temperature) make sure to reclassify the raster values to eliminate all areas, which

are expected to not be possible to grow palm oil. To do this go to “Spatial analyst tools >

Reclass > Reclassify”. The reclassification field will be the value of the raster. Then, input all

values existing in the raster file and make them all to stay the same except for value “1”,

which should be reclassified as “NoData”. Then generate the new reclassified raster. This

way, you will avoid having suitability scores (low or high) in areas where is not possible to

grow palm oil at all.

A mask of the study area should be used to define the processing extend and raster analysis

properties during the reclassification. This way, only values within the working area will be

taken into account and avoid errors.

Figure 2: Example of a model in Step 4

Step 5: Overlay analysis

In this step a suitability map will be generated by using a weighted overlay analysis. To do

this go to “Spatial analyst tools > Overlay > Weighted Sum”. In the tool select the weight that

each variable should have (we assign the same weight of “1” to all variables). Note: the tool

Weighted Overlay can also be used as it will standardize the results to the initial scoring

scale.

Figure 3: Example of a model in Step 5

The result should be a map only showing the areas with some degree of suitability. Non

suitable areas should appear as background or No Data (Fig. 8).

Figure 4: Sample result of a land suitability map for the West/East Sepik and Madang

provinces in PNG

Analysis and interpretation of results

The overlay results will produce a raster file with a range of values. Higher values represent

more suitable areas and lower values less suitable areas. Keep in mind that all areas resulting

from the overlay analysis are suitable to some degree; the non-suitable areas were already

eliminated in Step 3. You can decide how to assign the classes. In our case we divided values

in 6 classes with values of equal intervals. The number of classes and the method to assign

values to each class will depend on each analyst and the specific objective of the study. A

suitability map is just an initial screening tool for the assessment of the best areas where to

grow palm oil. It is required that results from a suitability map should be validated with

ground-level data in a further stage. Therefore, results from a suitability map are not

definitive and should be updated as new data becomes available.

Bibliography

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guineensis Jacq.) Cultivaiton:Journal of Environmental Science and Engineering.

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Temerloh and Kuantan, Pahang, Peninsular Malaysia. Journal of Birology,

Agriculture and Healthcare.

3. Ritung S, Wahyunto, Agus F, Hidayat H. 2007. Land Suitability Evaluation with a

case map of Aceh Barat District. Indonesian Soil Research Institute and World

Agroforestry Centre, Bogor, Indonesia.

4. FAO, 1976. A Framework for land Evaluation. FAO Soils Bulletin 32. FAO. Rome.

5. Shearman P.L, Bryan J.E. 2008. Papua New Guinea Resource Information Systems

Handbook 3rd Edition. University of Papua New Guinea. Port Moresby

6. Pirker J, Mosnier A. 2015. Global oil palm assessment. IIASA Interim Report. IIASA.

Laxenburg. Austria