GIS Applications

1. GIS Applications Faculty of Computer Science University of Indonesia Dr. Aniati Murni

2. Contoh Aplikasi • Agriculture Precision Farming • Electricity Distribution Network • Forest Fire

3. GIS in Agriculture Application(Source: GIS AsiaPacific, February/March 1998) Faculty of Computer Science University of Indonesia Dr. Aniati Murni

4. Aspects of Agriculture Application • Aspects of applications • Crop location and area estimation; • Yield prediction; • Diseases monitoring; and • Precision Farming. • Required information • Identification of crop type; Measurement of crop area; • Determination of crop boundaries; Estimates of yield and production; • The determination of crop health and stress; and • The measurements of in-field variation (good and bad crops).

5. Data Capture Technology(Pengaruh Image Resolution) • High resolution optical remote sensing sensor (SPOT, three times/year) • Synthetic Aperture Radar (SAR) sensor • High resolution image for precise farming: resolution-size vs error

6. Location and Area Estimation • Colors in optical sensor images can identify crop/vegetation type, different health and maturity, growing season and harvesting season (using multitemporal data); • Crop boundaries, image pixel size, and information from the grower can be used for area estimation. • Areas declared as potatoes areas is combined with the declared location and area of planted potatoes to get a map of growing potatoes. • Later, the field supervisor can use this map to record the planting returns and to process only the growing potatoes area with no corresponding planting returns.

7. Yield Prediction • Compare the probable crop yield at a detailed level and the product estimation; • Predict the surpluses or shortages to support price prediction and stabilization effort at national level; • Improve logistics planning for the harvest, transportation, storage and processing of seasonal crops.

8. Disease Monitoring • The healthy and unhealthy crops can be differentiated using false color optical sensor SPOT data where healthy trees are red and unhealthy trees are pink and purples. • This information can be used to estimate the impact on the production and model of the epidemic (in terms of extent, severity, spreading time). Red = healthy vigorous crops Pink/Purple = unhealthy or blighted crops

9. Precision Farming(Lower Cost but More Competitive) • The type and structure of soil or unsuited to the crop type; • Different nutrient availability or non-uniform of seed distribution; • Germination; and Uneven irrigation. Sources/Causes of in-field variation (good and bad crops): The usefulness of multitemporal analysis: • Is there any repeatable variation? • Then it can be solved by fertilizing (use only if it is required / reduce fertilizer use, efisien / saving money, avoid run-off chemical polluted water); and additional / better irrigation. • Can predict crop yield and help farmer to improve farming efficiency.

10. Vigorous Crops Recognition by Vegetation Index • Healthier crops higher vegetation index: ; Vegetation Index Measures High NDVI

11. GIS in Electricity Distribution Network(Source: GIS AsiaPacific, June/July 1998) Faculty of Computer Science University of Indonesia Dr. Aniati Murni

12. The Objective of the GIS Project • distribution network technical information base; • customer connection database; • and decision support systems. To improve the reliability and accessibility of the power company’s:

13. Information Requirements • An accurate register of geographic position of distribution system assets for valuation and financial performance reporting purposes; • The capacity and geographic location of each customer connection points to the network to be used as an input parameter in the determination of charges for line function services; • This meant that the management should be able to derive (a) an electrical schematic of the network for analysis and planning purposes; (b) an asset inventory with location attributes for system maintenance.

14. Steps to achieve the objective • Assess the existing information contained in database; • Conduct a survey on information requirements; • Design and implement a strategy to obtain all necessary information; • Coordinate all information into a central database of a GIS; • Develop operational processes and procedures to automate up-to-date data maintenance; • Expand access to and operations of the GIS throughout the functional units of the organization.

15. Identification of data requirements • No previous database or records of individual poles and structures; • Records on rural lines were limited to large scale hard copy maps (1:10.000), so that the line position information is inaccurate and no detail attributes; • Records on urban lines and cables were in the A1 paper of 1:10.000 scale and could be directly digitized using CAD system; • A number of text data should be entered including the code numbers of computerized transformer and substations;

16. Identification of data requirements (continuation) • Incomplete records of existing transformers and substation records without or with wrong transformer data; • The database was based on customer account which is related to a single meter installation, in fact one customer may have more than one meter installations, meaning may have more than one customer accounts; • The database could be based on customer connection points but there was no information on their load capacity; • The database for street lights was in text describing the attribute of location name.

17. Data Capture Strategy • Data of geographic position and load capacity of each customer network connection points; • Geographic position and attributes of substation, lines, cables, switchgear, and streetlights; • Data collection is divided into urban and rural data collection; • Rural data collection is divided into ground mapping / survey and aerial mapping; • Geographic position is measured using GPS (Global Positioning System).

18. Ground Mapping • A roving crew consists of one lineman and one surveyor, visits all rural transformer sites and all their connected customer NCPs. • The surveyor made a field sketch book of the layout of 400V lines from each transformer, detail 400V conductors, substation code numbers, GPS (geographic) positions. • The lineman affixed the NCP identification number plate. • At the end of each day, the GPS waypoint files were down loaded to CAD system, and the field sketch book was transferred to CAD mapping.

19. Aerial Mapping • The survey involved a low-level helicopter flyover of all rural high voltage lines; • A video camera is mounted at the helicopter and recorded the view of the overhead lines; • Each high voltage line connection is marked and its position is recorded and stored in the GPS file. • At the end of each day, the data is down loaded to CAD system to provide input data for high voltage line connection database.

20. Urban Data Collection • The existing urban power map of 1:1.000 scale is used in the survey as the field sketch map where additional customer NCPs information is added to the map; • The urban power lines include under-ground cable and above-ground poles; • The data of above-ground line network includes the customer NCPs and the related NCP code number; • The NCP map is used by the lineman to add information of meter installation code number and its load capacity; • In the case of under-ground cable, the customer NCP is located at the service pillar box, so that it can be identified; • All the data then are digitized into the CAD maps.

21. System Map Production • This system produces an electronic map of all components of line network assets and entities such as transformers, NCPs, etc.; • The electronic map consists of sets of graphic entities which are geographically connected to each other; • Entity identifier is needed to relate the graphic entity to its attribute (table data) in the relational database; • The tolerable accuracy for rural and urban network is 10m - 15m and around 5m, respectively.

22. GIS Implementation • After the electronic maps are produced, then the GIS construction can be implemented; • The data coverage/layers include the theme of high voltage network, NCP network, switch gear network, etc.; • The relation between the graphic/spatial data and text/attribute/non-spatial data is also established; • The system is then extended as a multiuser system, so that each unit in the company can utilize the data; • In this way, the system has already been integrated to the network operational management system.

23. GIS in Fishermen, Farmers, Forest Change and Fire(Source: GIS AsiaPacific, February/March 1998) Faculty of Computer Science University of Indonesia Dr. Aniati Murni

24. DSWR (Danau Sentarum Kalimantan Wildlife Reserve) Data Area : 132,000 ha Population : 6000 - 8000 Feature : Swamp Forest Fauna : Rare Red Asian Arowana Proboscis Monkey, Orang Utan Stakeholders : Muslim Melayu (fishermen, traders, timber concessionaires, timber workers, conservation agency and local government officials) Christian and Animist Iban (shifting cultivators)

25. Area of Danau Sentarum, Kalimantan

26. DSWR Conservation Project (1992 - 1997) • Funded by British Conservation Project between Government of Indonesia and UK Tropical Forest Management Programme (ITFMP); • Implemented based on Participatory and Community Management Plan in collaboration with the Ministry of Forestry’s Directorate General of Forest Protection and Nature Conservation; • Using the remote sensing technology and GIS (Geographic Information System) to produce 1:50,000 map scale; • Thematic Legend: vegetation, burned areas, regenerating areas, reserved villages (the local community decides its village boundary), socio-economic data, forest use status, administrative boundaries, geology data, logging concession boundaries, and spatial planning status.

27. Landsat TM of D. Sentarum (August 1990) Red = burned forest Green = forest Forest Burned Forest

28. Images of D. Sentarum Danau Sentarum in Wet Season Burned Swamp Forest

29. Joint Research between Conservation Project and CIFOR (The Center for International Forestry Research) • Forest Cover Change Analysis: using Landsat TM and MSS to produce base map of 1:50,000 scale. Aerial photo is used to digitize forest boundary on the base map. • Stakeholder activities: shifting cultivation (dry and wet land), hunting, fishing, harvest rattan, honey, firewood and timber from the forest. • During the period of 1973-1990, the swamp forest area was reduced from 4000 ha to 3444 ha; and the burned swamp forest was increased from 59 ha to 239 ha. Research on the Criteria and Indicator for Sustainable Forest Management System - Forest Change, Shifting Cultivator, Fisherfolks

30. Joint Research between Conservation Project and CIFOR (continuation) • The cause of forest fire could be physical (natural) or anthropogenic (the human) but it is sure not caused by shifting cultivation. • The hill and dry land forest (non-swamp forest) was also reduced from 1089 ha to 884 ha due to shifting cultivation activities. The remaining dry land forest was regenerated forest or non-cleared forest which is used for funeral. • Fishery could sustain the swamp forest, but some are also burned. The majority of the burned swamp forest are the dwarf and stunted (pohon-pohonnya kecil karena tidak subur) swamp forest.

31. Forest change of one village area

32. Reserved and Non-reserved Villages

33. Conclusions • Forest damages in non-reserved area have experienced greater loss in forest cover than that in reserved area. Most of the cause is fire. • Swamp forest damages in shifting cultivation areas are not always worse than that in fishery areas. • The remote sensing and GIS technologies are potential for sustainable forest management. • The socioeconomic data, ethnographic data and forest classification data could be used to obtain the cause of forest fire. • The shared claim zones consistently show forest decrease, often as a result of burning. Focusing on conflict resolution between the major stakeholders in these areas may lead to less burning. • The study of deforestation shows a complex analysis which need to examine conditions historically.