Post on 24-May-2020
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
1. Arshad, M, A. et al.2012. Evaluation of Climate Suitability for Oil Palm (Elaeis
guineensis Jacq.) Cultivaiton:Journal of Environmental Science and Engineering.
2. Arshad, M, A. 2015. Physical Land Evaluation for Oil Palm Cultivation in District of
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