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Geo-enabling Maori iwi to make better land management decisions

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We are currently working with several Maori iwi in New Zealand to improve their GIS capabilities and integrate spatial knowledge in their decision-making processes. The main aim of this work is enabling the iwi to create and manage their own GIS projects and use the results to enhance their day to day operations and long-term planning. In this pilot study on land use improvement with GIS and RS we created a spatial model to find the best land uses for a given land block and then integrated the results with profit forecasts in order to provide the landowners with a better understanding of the land capability. We used high resolution data captured with drones as well as freely available spatial datasets that we processed, reclassified, weighted and combined using ESRI ArcGIS software. The result is a series of land unit polygons, each representing a unique combination of possible land uses and relative forecast profit. This combined spatial and economic information provides a comprehensive understanding of the potential of a block of land. The modelling process is simple, flexible, clear to understand and easy to replicate, but more importantly it can be effectively used to make informed land management decisions.

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Geo-enabling Maori iwi to make better land management decisions

  1. 1. 1 Improving a collaborative pilot study with local indigenous organisations Elisa Puccioni Senior GIS Analyst at Kenex
  2. 2. Hamilton 14-16 May IT ALL STARTED HERE…
  3. 3. Why should I use GIS? Can it help understand my land better? How will it add value to my organisation?
  4. 4. Basic GIS skills + Basic GIS tools THE CHALLENGE: How to keep it simple?
  5. 5. Forestry AgriculturePasture Manuka Honey
  6. 6. $800 NZD/ha per year (harvest in 25 years) $600 NZD/ha (rolling hills agriculture) 1 hive per ha – average NZD/kg applied $2,000 NZD/ha (flats) $100k NZD/ha (blueberry) Parameters analysed Terrain parameters Current land cover Land use capability Erosion areas Water availability Water quality Soil characteristics Parameters missing
  7. 7. FIRST STEP, FLYING A DRONE… THEN, CREATING THE GIS
  8. 8. 1 FIRST STEP, FLYING A DRONE… THEN, CREATING THE GIS 1 2 3 4 5
  9. 9. 1 FIRST STEP, FLYING A DRONE… THEN, CREATING THE GIS 2 3 4 52
  10. 10. 1 FIRST STEP, FLYING A DRONE… THEN, CREATING THE GIS 2 3 4 53
  11. 11. 1 FIRST STEP, FLYING A DRONE… THEN, CREATING THE GIS 2 3 4 54
  12. 12. 1 FIRST STEP, FLYING A DRONE… THEN, CREATING THE GIS 2 3 4 55
  13. 13. 1 FIRST STEP, FLYING A DRONE… THEN, CREATING THE GIS 2 3 4 5 + LandCare LUC classes, river line, riparian margins, high risk erosion areas
  14. 14. 1 2 3 4 5 + LandCare LUC classes, river line, riparian margins, high risk erosion areas HIGH RES DATA LOW RES DATA
  15. 15. PUTTING ALL TOGETHER: Spatial Models Parameters ResultsBinary Maps 6 parametersFlat Land 7 parametersBlueberry 5 parameters Rolling Hills 2 parametersForestry 3 parametersHoney Reclass & Weight Areas with all the parameters presents Total ha calculated for each area Profit/ha calculated and applied Fuzzy Overlay AND
  16. 16. Final Land Capability Map $12.4K $7.3K 15.5 ha 9.1 ha $5.4K 4.5 ha $3.6K $9K 0.3 ha $250 $600 $30K $10k1.6 ha
  17. 17. The modelling process is simple and flexible, easy to teach. With it we can model for different land uses. We used only ArcGIS tools (and DroneDeploy) Landowners were happy of the increased knowledge of their land. WHAT WE LEARNED: Data at block resolution is hard to find, limiting the model accuracy. We love drones, but they are expensive and time consuming. We still need extensions and apps to complete the model ($$) Image interpretation can be complicated with basic GIS skills PROS CONS
  18. 18. 1 2 3 BUILD YOUR GIS PLATFORM CREATE DB & FIND MISSING DATA 4 5 LEARN THE TOOLS AND PRACTICE SHARE ONLINE! CREATE NEW PROJECTS & DIY!
  19. 19. Questions & Feedback Thank you! Moka Apiti

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