landform maps from this work form part of the national digital dataset now ... Methods of GIS Based Land Capability Assessment of Prime Agricultural Land (PAL).
GIS Based Land Capability Assessments of Prime Agricultural Lands (PAL) in Central Province of PNG Matthew Dell and Richard Doyle School of Land and Food, University of Tasmania, Hobart
The GIS component of the ACIAR Vegetable Project aimed to provide a method for identifying prospective land capable of sustained arable use (Prime Agricultural Lands - PAL) in Central Province of PNG. Introduction Accurate and reliable digital spatial and geochemical data on soil profiles is quite limited and patchy in Papua New Guinea with half-a-dozen or so CSIRO soil and land surveys completed in the 1980’s (Bleeker and Healy 1980 and Bleaker 1983, see Figure 1). The soil and landform maps from this work form part of the national digital dataset now available via the Papua New Guinea Resource Information System or PNGRIS. Initial work with the PNGRIS database examined the interrelationships of Resource Mapping Units (RMU’s), soil fertility, slope, aspect and inundation. This data was presented as a series of “Highest Agricultural Land Suitability” maps and tables for the areas of interest (Sogeri, Tapini, Laloki and Rigo) to NARI and the other project partners in 2011 as printed and digital outputs. Ground-truthing during field trips to our research trial sites indicated that the Land Suitability interpretations generated via this method were not being expressed as in either suitable soils or landscape positions on the ground. Further field examination and a review of the PNGRIS datasets and manual revealed that the soils dataset, and hence interpretive map layers, within PNGRIS is a highly extrapolated and modelled GIS dataset generated from a number of sparse and often spatially inaccurate and/or sporadic on-ground observations and datasets, i.e. less than 50 chemically analysed soil profiles in the whole of Central Province (Figure 1 and 1a). As part of this project key areas have been ground surveyed and 20 new soil profile observations recorded and 12 full analysed profiles completed (shown on Figure 1). The approximate locations of the original set of soil descriptions from the 1980’s work of Bleeker and Healy (1980) were digitised to help analyse and simplify the modelled PNGRIS soils dataset to provide a more practical representation of fertile soils within the study area. The coastal lowlands and adjacent elevated areas are known to have highly variable topography, including coastal plains, steep escarpments and dissected uplands/plateaux. Soils are also variable, with substantial areas of the coastal lowlands susceptible to inundation and steeper slopes overlain by shallower soils being susceptible to mass movement and slope wash erosion processes (Bleeker 1983, Hanson et al 2001). For expansion of agriculture in Central Province, more detailed understanding of the location of suitable soils and their topographic limitations is needed. Historically, this would have been achieved through interpretations from detailed soil surveys, reliable climatic layers, and landforms mapped using stereoscopic aerial photography. However, modern tools of Geographical Information Systems (GIS) with existing geological maps and radar imaging provide for rapid assessment of broad scale land capability interpretations for particular purposes. This assessment can be followed by ‘ground truthing’ and coupled with existing knowledge from, for example, field trials of crops and field data on soils, to assist in final assessment and decision making regarding agricultural development.
Figure 1.
Soil profiles with chemical analytical data and areas with GEOSAR Radar data coverage within Papua New Guinea. Note ‘Bleeker et al’ means data form the CSIRO soil and land surveys while ‘Aciar’ represents the 12 new soil chemically analysed profiles added to our research trial areas (ACIAR funded).
Figure 1a. Soil analysis sites, active climate stations and GEOSAR Radar data coverage within Papua New Guinea. Note “Bleeker et al” refers to the profiles analysed by CSIRO and reported in Bleeker and Healy 1980).
Methods of GIS Based Land Capability Assessment of Prime Agricultural Land (PAL) Papua New Guinea Resource Information System (PNGRIS) datasets were initially analysed and considered to be an over extrapolation of the natural resource data sets available, particularly as they relate to soils and soil properties. The main inputs for the modelled PNGRIS soil dataset are geology, climatic data, limited soil chemistry and a 90 metre DEM. It was decided given the large primary study area to focus on two broad scale areas that were considered most suitable for arable agricultural uses (Prime Agricultural Lands). These trial areas where centred about the Port Moresby and Tapini regions. The areas surrounding these two sites where roughly constrained by suitable Resource Mapping Units (RMU’s) within PNGRIS. These RMU’s are broad areas of homogenous geology, climate and topography compiled at a scale of 1:500,000 (Bellamy and McAlpine 1995). For the first stage assessment of Prime Agricultural Land in Central Province PNGRIS soil datasets for prospective fertile RMU’s was intersected with the PNGRIS Slope and Inundation datasets to provide a large merged dataset. The land seen as suitable for arable agriculture (Land Capability Classes 1 – 3 in system of Grose 1999) was constrained via the following limitations. STONINESS - Not to Moderately stony or rocky DEPTH - 50 cm to greater than 1 m DRAINAGE - Imperfectly to Well Drained TEXTURE - Medium to very fine ANION FIXATION - Low CATION EXCHANGE CAPACITY - Moderate to High AVAILABLE PHOSPHORUS - Moderate to High EXCHANGABLE POTASSIUM - Moderate to High BASE SATURATION - Moderate to High % TOTAL NITROGEN - Moderate to High SLOPE - Less than 10 degrees These attributes provided the first indication of where potential arable areas occurred. Unfortunately the soil chemistry, DEM, climate and geomorphic data used in PNGRIS were incomplete, poorly spatially located or of insufficient spatial resolution. Field validation of the datasets indicated other approaches may be more representative on actual suitable soils for agricultural expansion. Consequently, P-band GeoSAR radar elevation data, X-band GeoSAR Magnitude Radar Imagery, Regional Scale Geological Data and field observations of soils coupled with data from crop trial plots were used as primary data sources for the study. X-band and P-band radar data is collected concurrently from each side of a survey aircraft at an elevation between 10,000 and 12,500 metres. The X-band wavelength penetrates clouds and reflects from tree canopy to deliver surface model data in forested areas and accurate terrain elevation in open areas. The P-band wavelength penetrates both clouds and tree canopy to deliver terrain elevation and surface feature extraction in forested areas. These characteristics make GeoSAR ideal for mapping large areas of mixed land cover particularly in Tropical areas such as Papua New Guinea (Williams and Jenkins 2009). The regional scale geological data provides the only credible bedrock information available for the selected study areas. Tiled P-band radar surface points which penetrate all but the densest vegetation provide a high resolution model of the terrain. The points were provided by the Australian Defence
Imagery and Geospatial Organisation as ASCII point data with spacing of 2.5 metres, and were gridded to a mosaic of 5 m Digital Elevation Model (DEM) surfaces using the ArcGIS “3D Analyst” extension. This data provides a more accurate and higher resolution representation of the local topography than the publically available 90 metre Shuttle Radar Topography Mission (SRTM) data for the study area. The release of the reprocessed SRTM data in March 2015 enabled the comparison of the GEOSAR 5 metre, SRTM 30 metre and PNGRIS 90 metre datasets and provides a mechanism to produce similar outputs across the entire country. From the detailed elevation model data a four class Topographic Position Index was generated using Land Facet Tools Extension for ArcGIS (Jenness et. al 2011). This extension divided the topography into Ridges, Upper Slopes, Lower Slopes and Valleys. Using this classification ‘Lower Slopes’ of less than and equal to 10o were selected as potential suitable sites for intensified arable agriculture as they are considered ‘very gently to moderately sloping’. Land less than 10o or 18% can be classified as falling within Land Capability Classes 1 – 3 i.e., ‘arable cropping lands’ or Prime Agricultural Land (PAL) in the Tasmanian Land Capability classification systems (Grose, 1999) and similar systems used in the USA and NZ (Ministry of Works, 1979). Slopes above 10o gradient are more susceptible to mass movement (landslides) and rill and sheet erosion due to the intense high precipitation events experienced during the wet season. The valleys and associated low angle plains below and between these ‘Lower Slopes’ while providing good potential for arable agriculture where not included in our this initial analysis due to high prevalence of flooding, inundation and waterlogging as indicated by PNGRIS. This broad topographic classification was further constrained by the lithology or soil parent material underling the previously identified lower slopes. The area’s deemed most suitable for intensified agricultural production were identified as those areas underlain by high nutrient containing parent rocks like intermediate or mafic lithologies (basalts, gabbros and related rock types) or derived alluvium and colluvium which provide for deeper and base rich soil parent materials (see Table 2). Thus their derivative soils such as Ferrosols (iron rich structured clayey soils), Dermosols (structured soils) and Vertosols (reactive clay soils) (Australia Soil Classification) or Andosols, Inceptosols, Vertosols, Mollisol and Oxisols (USDA Soil Taxonomy) generally provide the potential for the more productive and sustainable agricultural lands. Limited numbers of soil profiles were described and soil types noted in road cuttings and gardens in the district to verify our interpretations. Results This broad first-pass classification using the GEOSAR radar data identified 48,394 ha of land for potential agricultural expansion within the Port Moresby study area in Central Province (Table 1). A further 13,664 ha of land is subject to various levels of inundation and waterlogging. The land was underlain by a wide range of parent material/bedrock, though most were of volcanic origin dominated by gabbro (see Table 2 and Figure 2). The recently acquired publically available SRTM (Shuttle Radar Topographic Mission) 30 m DEM was processed using the same methodology as the GEOSAR radar data and the results were very similar with the largest discrepancies observed in Aroma Rural and Kairuku Rural areas (Table 1) due to the inclusion of areas not covered by the GEOSAR data. The majority of this land is centred on the town of Kwikila some two hours by road south east from Port Moresby. Of the 48,314 ha identified some 13,664 ha is listed within PNGRIS as being prone to waterlogging and inundation of varying duration and severity (Table 3). Of the 13,664 ha susceptible to inundation 4,146 ha (Inundation types 1, 2, 4 and 6) would probably be excluded from agricultural use.
Table 1. Potential suitable arable agricultural land around Port Moresby, Central District Papua New Guinea as constrained by GEOSAR, SRTM and PNGRIS data. GEOSAR 10m No flooding or inundation Long term inundation
AROMA RURAL
HIRI RURAL
KAIRUKU RURAL
KOIARI RURAL
RIGO CENTRAL RURAL
RIGO COASTAL RURAL
RIGO INLAND RURAL
TOTAL
1209
5123
3048
10380
16448
265
11920
48394
7
31
28
2
2661
84
2812
6
6
783
5805
Near permanent inundation Periodic brief flooding
63
Permanent inundation Seasonal inundation
571
758
2 24
Tidal flooding
782
2822
27
665
152
101
342
255
6
667 3220
209
3713
64
661
TOTAL
1303
6222
4190
11835
25151
293
13065
62058
SRTM 30m
AROMA RURAL
HIRI RURAL
KAIRUKU RURAL
KOIARI RURAL
RIGO CENTRAL RURAL
RIGO COASTAL RURAL
RIGO INLAND RURAL
TOTAL
2922
5083
6734
11248
16411
278
12380
55055
7
32
31
2
2532
85
2688
6
6
842
5915
No flooding or inundation Long term inundation Near permanent inundation Periodic brief flooding
106
Permanent inundation Seasonal inundation
27
Tidal flooding
550
838
849
2
45
947
155
165
6
332
252
2702
29
993 2958
226
3538
59
643
TOTAL
3062
6154
8064
13052
24602
307
13599
68839
PNGRIS 90m
AROMA RURAL
HIRI RURAL
KAIRUKU RURAL
KOIARI RURAL
RIGO CENTRAL RURAL
RIGO COASTAL RURAL
RIGO INLAND RURAL
TOTAL
13580
5885
3758
18851
3561
3086
9133
57853
1
2485
1143
26
990
7
246
4898
No flooding or inundation Long term inundation Near permanent inundation Periodic brief flooding
455 2081
Permanent inundation
3896
1928
2
1
Seasonal inundation
3
2043
418
Tidal flooding
2
6
4
15666
14316
7707
TOTAL
455 882
1581
2618
899
13885 3
89
1430
12
454
9 19848
7561
5732
4448 21
10732
81562
Table 2. Geological bedrock and associated areas of land assessed as suitable for agricultural development using GEOSAR data about Port Moresby, Central district, Papua New Guinea Geological Bedrock Andesitic and basaltic vitric, crystal, lithic tuff, minor agglomerate; partly calcareous; strongly jointed
Area (Ha) 41
Basalt and andesite pyroclastics, lava, volcanic sandstone
3378
Basalt and andesite pyroclastics, minor lava: remnants of cappings
2360
Basalt and andesitic agglomerate, minor tuff; tuffaceous sandstone and volcanic conglomerate at base
624
Basalt and minor andesite agglomerate and tuff, partly reworked
8586
Basalt and minor andesite agglomerate, tuff, lava, lava breccia, partly reworked
4008
Basalt and pillow lava with gabbro and dolerite intrusives (dykes), minor calcilutite
684
Basaltic and andesitic agglomerate and lava: shoshonitic affinities; volcanic plugs
218
Basaltic and minor andesitic agglomerate, tuff, lava, lava breccia, with intercalated volcanically derived conglomerate and sandstone
63
Diorite and porphyritic microdiorite, monzonite and granodiorite stocks. Oveia Diorite equivalent
25
Gabbro, diorite and other acid differentiates to the west; fine-grained gabbro, dolerite and basalt to the east
25626
Gravel, sand, silt, mud, clay: alluvium and beach deposits
1973
Lavas and pyroclastic rocks of the Mount Cameron Range volcanic cone
379
Massive green mafic schist derived from basalt, dolerite, gabbro, & volcanic sediment; minor calcareous & felsic schist or phyllite.
64
Porphyritic, vesicular basalt and andesite lava; shoshonitic affinities
91
Volcanic conglomerate, tuffaceous sandstone, minor siltstone; moderately consolidated
273
TOTAL
Figure 2.
48 394
Potential suitable Arable Agricultural Land as constrained by geology and SRTM 30 m DEM (areas in Blue) in Central Province PNG. (Note – this layer excludes alluvial landforms prone to flooding constraints – see Figure 4 show alluvial area inclusion).
Figure 3.
Potential suitable Prime Agricultural Land (arable) as constrained by PNGRIS and SRTM 90 m DEM (areas in Yellow) in Central Province PNG
Table 3. Areas of land subject to inundation about Central District Inundation Type 1. Long term inundation 2. Near permanent inundation 3. Periodic brief flooding 4. Permanent Inundation 5. Seasonal inundation 6. Tidal flooding TOTAL
Area (Ha) 2,812 6 5,805 667 3713 661 13,664
While this method provides a guide on where potential arable agricultural land occurs, the various models could be dramatically improved with more detailed geological, landform and inundation inputs, e.g., based on catchment flood flow data/modelling, and a comprehensive on ground soil mapping program. There are many areas of alluvium and colluvium that are currently modelled as being subject to inundation that appear to be highly prospective for and expansion of agricultural use. We also provide a further extrapolated Prime Arable Land layer in Figure 4 which includes these alluvial soils on active floodplains to show areas where small-holder gardening could and can be undertaken, with suitable alluvial soils, but this may be subject to seasonal flooding and consequent crop losses (Figure 4). This was in part based on our observation that much current gardening occurred on such landforms despite potential inundation hazards. By incorporating these alluvial soils into the analysis within Central Province an additional 31636 hectares of potential PAL is identified within the 7 sub-provinces documented in Table 1.
Figure 4.
EXPANDED - Potential suitable Arable Agricultural Land (PAL) as constrained by geology and SRTM 30 m DEM (areas in Blue) in Central Province PNG. Note in this layer we have included many alluvial landforms formerly excluded due to flooding hazards. However these soils are often of a fertile nature, due to mixed mineralogy and annual siltation, and they have been included in this layer to show the maximum extent of Prime Agricultural Lands (Classes 1 – 3), despite this flood risk to production.
GIS Training of NARI and other agricultural officers We believed it important to extend some of the GIS based land resource mapping learnings and resources to local agricultural officers in PNG. This is because this work is often iterative in nature and uses changing or upgraded data layers as their availability and resolution changes (improves). So during February 2015 a week-long GIS training workshop was delivered to 17 PNG participants from the projects partner organisations at the NARI research station in Lae (mainly NARI and FPDA but also Palm Oil and Coffee Institute officers). The training course provided a comprehensive introduction to the open source QGIS system and provided hands on practical instruction on common data collection and mapping tasks. Participants were provided with a copy of the QGIS software along with a number of other useful open-source software packages plus the full set of the PNGRIS GIS coverage’s for their respective provinces. The training course was customised such that all the course datasets were from Papua New Guinea, either Port Moresby or the area immediately surrounding the Research Station at Lae. This enabled a much quicker uptake of the technology with the datasets providing context for the complex tasks being undertaken (Figure 5).
+ Figure 5. Participants hard at work at the QGIS Training Course, Lae February 2015
Figure 6. Participants during field exercises at the QGIS Training Course, Lae February 2015 All workshop participants were instructed in the collection and collation of field data, collecting spatial locations and tracks using GPS handsets and then transferring the attributes into the GIS software. On completion of the course the participants were able to produce a map applicable to their respective jobs as high quality outputs (Figures 5 and 6). The participants had a variety of past experience in GIS software and there was only one participant who struggled to complete all the allotted tasks during the training due to missing the first day of orientation and set-up. He is now working through the training materials in his own time with the help of his fellow participants using his own data, and is keen for some more training. Overall the course was very well received with the participants arriving before the daily sessions began and working through their lunch break to maximise their time where the instructors could answer the many questions that arose.
Please rate the applicability/relevance to your workplace 10 9 8 7 6 Strongly disagree
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1 0 I will apply this I feel confident to I am now able to I will continue my I am motivated to training in my day assist others the interpret materials personal incorporate GIS in to day work use of GIS software developed by using development in GIS work plans and GIS by seeking proposals I initiate additional training
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Figure 7.
Training manuals
Field exercises
The whole course generally
Feedback metrics from the QGIS Training Course. The feedback forms (available in MS-Excel file) were very informative and all participants expressed a desire for more comprehensive QGIS training.
Data Provision and on-going GIS help service Following on from the training course in Lae the participants set up an online users group where that can post questions or ask for help about all things GIS. This group and the mailing list associated with it has been used participants to further their skills and continue to help each other with their new found enthusiasm for GIS. Through this forum and via email
participants regularly make contact with software issues or help with problematic datasets and it is anticipated that this will continue in to the future. During mid 2015 a series of new high resolution elevation datasets covering all of PNG where made publically available. This data was processed in Tasmania as part of the ACIAR project and made available to all the workshop participants in convenient province sized subsets. For many it is the first time that they have been able to access high resolution elevation data for their respective provinces or study areas. The data is already being used to generate accurate contours, develop drainage models and aid in other Natural Resource Management issues and projects. The high resolution Radar elevation data acquired from DIGO for the project only covered around 50% of Central Province. This new publically available elevation dataset also enabled a comprehensive model of potential arable agricultural land for the whole of Central Province to be produced. This also means the model could now be expanded to delineate Prime Agricultural Lands across PNG. This new elevation data along with other data produced during the project and data processed for the range of PNG Agricultural Officers is being distributed via Dropbox. This enables agricultural officers to access new and updated data or get assistance from other GIS trained participants in Port Moresby and Lae headquarter offices to obtain them on their behalf using the higher internet speeds at those locations.
Discussion The GIS data and radar imagery has been combined to produce informative maps that can be used to prioritise areas for sustainable agricultural development, i.e. Prime Agricultural Land (PAL) of Land Capability Classes (1 – 3) inclusive, i.e., arable lands. They have clearly identified potential areas, and by relaxing or tightening the constraints set when using the radar imagery, the area of potentially useable land might be increased or decreased. For example, if the allowable slope was reduced to say, 7o for a particular agricultural system in which soil cover was limited between crops, or even within that crop (e.g., onions) and thus increasing the erosion risk, the area of suitable land available could be re-mapped and would decrease accordingly. Conversely, if the assessment was being made for land uses involving perennial pastures, forestry and fruit trees the allowable slope limits could be increased, resulting in larger areas of potentially useful land being identified. Hence the need for training of staff in PNG in the process of iterative GIS analysis and reporting. Indeed further training would be warranted and travel to Australia or undertaking of postgraduate studies would be most beneficial. The present analysis has identified extensive areas of Prime Agricultural Land in the Central Province. When combined with ground based observations along roads and in village gardens augmented with limited examination of augured soil profiles, the approach of using GIS and radar imagery is proving a very useful tool for assessment of broad scale land capability assessment of Classes 1 – 3 inclusive (PAL). Our team has also applied this approach in several other areas in Central Province, with similarly useful output, again with initial validation from ground based observations and soil data. Nevertheless, the approach must be complemented with other detailed and temporal information, such inundation frequency, scale and duration, to gain a more accurate assessment of land capability and guide development and agronomic decisions on crops to be grown and practices used on specific sites.
Figure 8.
Potential suitable areas for arable agriculture in and around Rigo, Central District Papua New Guinea (Blue)
Clearly, there are significant areas of suitable land available for agricultural development in Rigo district (Figure 8), and other areas of Central Province and beyond. However, for effective development, appropriate agronomic practices will also need to be developed; these being part of other work being conducted by the broader project. Land tenure issues notwithstanding these data will assist the sustainable agricultural development process in PNG and increase employment and business prospects for local farmer cooperatives. The projects efforts at training a greater number of GIS capable government officers also offers the potential for application, further interpretation and use of these data sets. It is worth noting some efforts to improve the layer registration errors in PNGRIS might also enhance use of this dataset for PNG lands and their sustainable uses in to the future. A new worldwide elevation dataset became publically available in March 2015, called Shuttle Radar Topographic Mission or SRTM. This 30 metre resolution dataset provides a very useful national dataset for Papua New Guinea. The project team has processed this dataset and produced Digital Elevation Models for each of the Provinces and has been distributed to representative of all the member organisations. This national dataset provides the capacity to apply the same methodology as used with the GEOSAR data to provide a comprehensive regional scale analysis of suitable locations for agricultural intensification. The SRTM dataset and the GEOSAR datasets provide the opportunity to update the numerous other PNGRIS layers that were reliant on the previous 90 metre SRTM dataset. The GIS training provided a chance for a skills transfer to a group of people who actively embraced the software and could see immediate application of what they learnt to their work. The course also gave the chance to ‘upskill’ some existing GIS users to a position where they did and can help instruct others. Follow-up training is recommended and has been actively requested by participants.
Conclusions We recommend using high resolution radar generated topographic coverage’s in combination with soil parent material classification based on the mapped bedrock lithology as a base to generate more reliable broad scale maps of Prime Agricultural Lands (Land Capability Classes 1 – 3). This methodology can show national and local government development bodies, aid agencies and the village farmer cooperatives the potential areas available for land use intensification and sustainable national agricultural development. The maps would then be combined with other local data to provide a sound basis for development decisions and to guide agronomic practice and infrastructural development. Where the radar topographic coverage is unavailable the 30 metre global Shuttle Radar Topographic Mission (SRTM) data provides an accurate alternative. Some care should be taken when using the existing PNGRIS GIS datasets. There are some serious data accuracy and corruption issues that will prove very frustrating for an inexperienced GIS user. Some thought should be given to a review and update of the PNGRIS database as while it has some major issues for the most part it is the only comprehensive resource for farmers and planners alike. References Bellamy, JA and JR McAlpine (1995). Papua New Guinea Inventory of Natural Resources, Population Distribution and Land Use Handbook. Commonwealth Scientific and
Industrial Research Organisation for the Australian Agency for International Development. PNGRIS Publication No. 6, Canberra. Bleeker P (1983). Soils of Papua New Guinea. CSIRO Publishing, Canberra. Bleeker P and Healy PA (1980). Analytical Data of Papua New Guinea Soils . CSIRO Publishing, Canberra. Bryan JE and Shearman PL (2008). Papua New Guinea Resource Information System. University of Papua New Guinea. Port Moresby Grose CJ (1999). Land Capability Handbook – guidelines for the classification of agricultural land in Tasmania. Published by the Tasmanian Department of Primary Industries Water and Environment. Hanson LW, Allen, BJ, Bourke RM and McCarthy TJ (2001). Papua New Guinea Rural Development Handbook. Research School of Pacific and Asian Studies, Department of Human Geography, Australian National University, Canberra. Jenness J, Brost B and Beier P (2011). Land Facet Corridor Designer: extension for ArcGIS. Jenness Enterprises. Available at: http://www.jennessent.com/arcgis/land_facets.htm NZ Ministry of Works and Development (1979). Our Land Resources. Crown Copyright,
Wellington New Zealand. Williams ML and Jenkins LG (2009). GeoSAR and DBInSAR Combining “X” with “P" for Tropical Forest "C". Photogrammetric Engineering Remote Sensing (2009) Vol75, No7, 738-743p.
