Grant Proposal. Mangrove Restoration in Indonesia using GIS

Transcription

1 Grant Proposal Mangrove Restoration in Indonesia using GIS Andrea L. Anderson May 2012 Background: Mangrove ecosystems are an integral part of coastal zones around the world, but their deforestation necessitates targeted solutions to delineating their extent, condition, and restoration areas of highest success probability. According to the United Nations Environmental Program (UNEP-GPA, 2004), 18% of the Earth s surface is coastal zone with 60% of the world s human population. Mangroves comprise 8% of that coastal zone and act as important transitional environments between land and marine. Coastal areas are often protected by mangroves that serve to control erosion and attenuate the effects of storms and tsunamis, such as the Indian Ocean tsunami of December They are characteristic of tropical regions subject to the action of tides, woody tree species and many micro and macroalgae adapted to fluctuations in water salinity, shifting sediments with low levels of oxygen (Ximenes et al. 2007, 4331). Such ecosystems are important biomass producers and sources of food, medicine, fuel and building material for local communities (Giri et al. 2008). Mangrove flora was even identified as donor of salt-tolerant genes that can be utilized for salinity-resistant crop varieties (Selvam et al. 2003, 794). Mangroves provide protection and food for juvenile fish, which is important for fisheries. Tourism activities often take place in and around the mangrove wetlands as well (Hossain et al. 2009). Unfortunately, there are a number of problems facing mangroves. Climate change and its resulting erosion, sedimentation and sea level rise is but one factor. Other problems include pressures of increasing populations, food production, industrial and urban development and wood chipping (Field 1999, 47). In mangrove areas, fishing and farming may be the main activities, but mangrove cutting is often a secondary occupation (Kairo et al. 2002, 156). In Kenya, as in many other coastal countries, degradation is directly reflected in increased coastal erosion, shortage of building materials and firewood, and reduction in fishery (Kairo et al. 2002, 154). Where resources are overexploited, there is also the problem of chemical contamination, especially in aquaculture in many Asian countries. Asia has lost 12% of its mangroves from due to agricultural expansion (81%), aquaculture (12%) and urban development (2%) (Giri et al. 2008). As of 2005, Indonesia had 68,194 ha of mangroves, but the country was losing these areas at a rate of (-0.33%) annually (Giri et al. 2008). While aquaculture is slowing in growth in many other countries, it is still growing in Indonesia. Abandoned shrimp farms leave a lot of toxic chemicals that stifle restoration of these areas. A main deforestation area in Indonesia is Langsa, an area that I would like to concentrate on. 1

2 Due to the importance of mangroves, a number of Asian countries have launched ambitious conservation and rehabilitation programs, such as in India and Bangladesh. A major effort was made beginning in 1996 on the east coast of India (Selvam et al. 2003). Bangladeshi efforts have been highly successful since planting began on a large-scale in 1966 (Giri et al. 2008). While there are a number of national forest programs that protect mangroves by prohibiting most kinds of activities in those areas, small isolated patches are often not protected and are managed by local communities. The goal of restoration efforts should be to rehabilitate the mangroves to support human communities with a mix of mature forests, logged forests, aquaculture and agriculture (Field 1999, 47). Unfortunately, most forest-dependent communities are left out of planning and protection projects. GIS is a good tool for mapping the extent of current mangrove populations, their condition, for selecting restoration sites, and for monitoring the effectiveness of restoration efforts. GIS can be updated rapidly and derived from multiple sources, and comparative analytical work can be done. Specifically for mangroves, GIS works well, because ground based techniques are of limited use in mapping mangroves due to the difficult terrain. GIS can quantify extent, structure and development of mangrove ecosystems. Effective management of mangrove resources requires frequent and spatially detailed assessments of species number and distribution, so a blend of remote sensing and GIS provides accurate and reliable information on mangrove extents and rates of change at a relatively low cost. Literature Review: In the late 1990s, efforts were taken to map the extent of mangroves around the world. The compilation of a world mangrove atlas was to be the base-line against which future trends could be measured (Spalding et al., 1997). The study revealed a paucity of reliable data. More effort was needed at the level of individual countries to catalogue the extent of their mangroves, so the next decade saw increased work in mapping mangroves and their changes over time. As of 1999, the use of remote sensing and GIS in the actual rehabilitation of mangroves was, however, practically non-existent (Field 1999, 51). Geographers created maps but not targeted solutions to rehabilitation. Eventually, GIS gained momentum as a management tool, as shown by a 2002 study in Kenya. Kairo et al. (2002) studied the status of mangroves within and adjacent to the Kiunga Marine Protected Area in Kenya. Before 2002, the application of GIS in Kenyan mangrove management was non-existent. They assessed mangroves by means of aerial photographs and intensive ground truthing. Vegetation maps derived from the aerial photographs provided classification based on tonality, crown texture, structure, tree height, and relative position on the ground easily distinguished different species of mangroves. Not just the extent but the condition of the mangroves was assessed by stratifying mangrove-forested areas into productive and nonproductive classes. Productive areas had stem density of more than 40% and tree height exceeding 5m. The results showed excellent prospects for sustainable development in the 2

3 productive forests. Sustainable development would help protect the forests and the community. The researchers argued that the potential yield of future mangrove forest can be gauged by an evaluation of current standing volume, but beyond comparing forest densities to a few other Asian countries, this point needed more elaboration and perhaps time data. Another study in India (Selvam et al. 2003) actually looked at the effectiveness of restoration efforts over time. The researchers used remote sensing to assess the success of mangrove wetland restoration in Pichavaram mangrove. Images from Landsat 5 TM digital data (1986) and IRS 1D LISS digital data (2002) and Survey of India toposheet produced mangrove wetland maps for the years 1986 and Similar to the Kenyan study, mangrove forests were stratified into classes based on color, tone, texture, pattern, size and shape. The resulting classes were: dense mangroves, degraded mangroves, young mangrove stands, barren sand dune associated with mangrove wetlands, vegetation associated with sand dunes, water body and dry land. All mangrove classes should not be lumped together. Stratifying them allows for targeted restoration plans. The study attributes the 90% success rate to the user-communities, who in addition to protecting and assisting with restoration efforts also harvested 245 tons of fish, prawn and crab from the area annually. The study serves an example of having community investment in an area. A 2007 Brazil study (Ximenes et al.) provides an excellent framework for site suitability selection when planning where restoration should take place. Using Itaipu lagoon, RJ, as their study area, the researchers developed a GIS supported method for the selection of suitable areas for the successful planting of new man-made red mangrove areas. I will be using this method heavily in selecting restoration sites in Indonesia. They used three environmental variables, sediment type (particle size), soil organic matter content and interstitial water salinity to produce a suitability areas map of the lagoon for the red mangrove, which has specific habitat requirements. The three variables were weighted equally, but I will consult more with field scientists to figure out how the environmental variables should be weighted according to specific species in Indonesia. This study also does not classify the mangrove areas into sub-categories, such as degraded mangroves. Efforts should be made in existing mangrove areas, not just where there are none. The results of the suitability map were not validated due to limited resources, but part of my study is to include remote sensing monitoring over time. In 2008, researchers estimated the present extent of tsunami-affected mangrove forests in Indonesia, Malaysia, Thailand, Burma, Bangladesh, India and Sri Lanka and determined the rates and causes of deforestation from 1975 to 2005 (Giri et al. 2008). They interpreted time-series Landsat data using a hybrid supervised and unsupervised classification approach. There was insufficient ground truth data for a purely supervised classification. Once the change maps were produced, areas of change were validated with the help of local forestry experts and/or highresolution commercial satellite data. This study showed how to measure rates of change of mangrove forests. They argued that past studies failed to map the extent and rate of change with sufficient detail; past regional studies used 1 km or coarser. This study used moderate 3

4 resolution, resampling to 50 m. The spatial and temporal detail was better than previously available. In crunching through the large volume of regional data, the researchers chose simple and efficient analytical methods to see change over time. Secondary information from local experts was also important in their analysis. While this study mapped existing mangroves, I would like to do a smaller-scale study that maps stratified classes of mangroves to target restoration projects. Hossain et al. (2009) narrowed down their study of land change to Thailand, assessing spatial and temporal landuse/cover changes in and adjacent to marine protected areas. They focused on eight major landuse classes, one of which was mangroves. Again, the condition of the mangroves was not described, only their presence or absence. In my study, I would like to know better what condition the mangroves are in. The researchers used satellite images, aerial photographs and GIS data to demonstrate considerable changes, especially an increase in agriculture, in the whole study area but especially within the protected area boundaries. The mangrove areas experienced negative changes. Protected status did not translate into complete protection, so the authors of the study discussed the utility of buffer areas where human activity would be restricted. I thought greater detail on the socioeconomic context of the surrounding communities and their land development structure was needed. The end result of understanding the status of mangroves and how communities fit into them is to affect policy. According to Roy et al. (2012), there is a missing link between conservation and livelihood security of forest-dependent communities. They examined four historical management periods in the Sundarbans Mangrove Forests all the way back to It was not until 1994 with the National Forest Policy that the interests of marginalized communities were considered for the first time but an overall conservation policy blocked any follow-through to that commitment. The forest continues to be treated as a common public good. There is a need to foster state-people partnerships with clear distributed stakeholdership. Policies with that as a foundation will move beyond controlled access by permit holders. Countries, like India and Indonesia, need to find alternative native property rights regime. Hypotheses: What is the extent of mangroves in Indonesia, especially in Langsa, and what are their conditions? How to select suitable restoration sites? How to monitor the effectiveness of restoration efforts? Data: I will start by collecting data, such as toposheets for basemaps and maps of environmental variables (most likely in vector format), from the following Indonesian government departments: Department of Forestry and Estate Crops, Directorate General of Chemical, Agriculture and Forestry Industries, Directorate General of Geology and Mineral Resources, and State Ministry for Environment / Environmental Impact Management Agency. 4

5 The data may already exist in GIS form, but other ancillary data, such as tsunami reports, will be important to assess condition. When possible, I will convert and process the information into geospatial data or layers. There is a strong selective pressure over individuals of various mangrove species. Vegetation maps can be derived from aerial photographs, because classification based on tonality, crown texture, structure, tree height, and relative position on the ground easily distinguishes different species of mangroves and even their conditions (Kairo et al. 2002). I will focus on species that already thrive in Indonesia. I will consult with mangrove forestry experts in Indonesia as to how best assess site selection criteria, such as water salinity, winds, temperature, dryness and inundation (Field 1999, 49). For example, the red mangrove likes rich, saline soils with very fine sediment particles (Ximenes et al. 2007, 4333). Field work will be required to collect environmental variables at sampling stations established with a GPS. Local environmental conditions must dominate all other considerations (Field 1999, 49), so field trips to obtain first-hand knowledge on local environment and vegetation coverage types are important. This may mean visiting local forestry groups with hard copy maps. The intuitive knowledge of locals will be converted to geospatial data. In addition, aerial photography is necessary for understanding intertidal zones, vegetation cover, such as existing mangrove patches, sand dunes, and urban areas. In order to monitor change, Landsat raster data will be used to map the present extent of mangroves in Indonesia and then used in the future to see how effective restoration in targeted areas will have been. Possible sources of satellite imagery are Landsat GeoCover (generally cloud-free images) and Enhanced Thematic Mapper Plus (ETM+) from US Geological Survey ( Commercial data from high resolution satellites, like QuickBird and IKONOS, will be needed for future validation work, as well as visiting local forestry groups with hard copy maps of land changes to get their input. Methods: What is the extent of mangroves in Indonesia, especially in Langsa, and what are their conditions? How to monitor the effectiveness of restoration efforts over time? The method for understanding the mangroves current extent will be the same method used for understanding its change over time with the addition of change analysis. First, I will focus on field data collection with geo-referenced photos, maps, and local information. Then, I will use Landsat data geometrically corrected with ground control points, such as road-road intersections, to an accuracy of plus-or-minus half a pixel, which is necessary for change analysis later on. Satellite images can be normalized for solar irradiance by converting digital number values to the top-of-the- atmosphere reflectance. This was tested on mangrove areas in the Sundarbans by Giri et al. (2008). Ground truth data, existing maps and data bases are used to select training samples. I would like my ground truth validation accuracy at 95%. Structural 5

6 attributes like tree height, basal area, density and species composition characterize mangrove communities and can provide different levels of conditions. Healthy mangrove forests versus degraded mangrove forests require different community efforts. Restoration efforts can be tailored to fit both degraded mangrove forests and areas where mangroves have disappeared. For viewing mangroves over time, I will use a post-classification change detection approach that provides from-to change information. This is also where secondary data is used to help, but secondary data is not always consistent across areas or time. In addition, local information is not always conducive to being turned into geospatial data. The change maps can be validated with local experts and high-resolution commercial satellite data. Remote sensing combined with GIS is a monitoring tool for restoration projects that can show how many mangrove patches have been lost or successfully restored. How to select suitable restoration sites? After gathering all available photo, GIS, and map data, I will start by georeferencing scanned aerial photographs (Ximenes et al. 2007). I plan on identifying at least 200 ground control points (GCP) using a Garmin hand-held device during field work. Another 50 GCPs can be obtained from topomaps and even local admiralty charts. I will georeference to UTM projection coordinates, because they create a square grid, no negative numbers, and they are measured in metric units. Based on the Ximenes et al. (2007) study, at least three environmental variables need to be sampled in the field: particle size, organic matter content, and interstitial water salinity. I will add variables and weight them according to recommendations by forestry experts. There will be 150 sampling stations distributed randomly across the study area. I will interpolate values over the total surface and then classify the values into categories based on ranges for the environmental variables. Particle Size Organic matter content (%) Salinity (PSU) Silt/clay Very fine sand Fine sand Medium sand I will exclude areas of existing mangroves and areas outside of intertidal flats with a mask. ArcGIS s Geoprocessing wizard will be used extensively to get shape files only for the selected study area. 6

7 Suitable areas will be identified by multiplying all the suitable areas of each environmental factor considered: Suitable areas = (Suitable particle size * Suitable Organic * Suitable Salinity) * (Intertidal areas * No mangroves) Once I get the suitable areas, I can reclassify these into size classes for concentrated restoration efforts: small, medium, or large areas. Pixel groups less than (<0.08ha) will be ignored for the final suitability map. The final map is useful for estimating how cost/number of propagules will be needed based on a 2x2m grid spacing. Finally, it is important to then validate results over time. Anticipated Results: I expect to find a lot of fragmented mangrove areas with degraded conditions due to local pressures. However, my methods will highlight those areas of highest success probability. NGOs and local mangrove restoration projects may already corroborate my future findings for site selection since efforts are ongoing. Over time, my study will also show how much loss of biodiversity can be tolerated in order to maximize productivity and ensure system integrity since I will be focusing on mainly monothetic mangrove forests (Field 1999, 49). My measures of success will be effectiveness of planting, rate of recruitment of flora and fauna, efficiency of rehabilitation, and long-term sustainability (Field 1999, 47). Policy Applications: Because mangroves are important for saving lives and property, restoration efforts have been going on for decades. However, conservation remains a choice, often determined by socioeconomic priorities of the communities involved but also often influenced by pressure from conservation organizations advocating preservation of nature (Field 1999, 47). The existing top down approach to policy making for mangroves makes it difficult to implement robust laws and regulations that are not in odds with communities currently living in them (Hossain et al. 2009, 1093). User-communities in degraded areas were mainly responsible for restoration success in Pichavaram, India, by desilting the artificial canals dug wherever needed and protecting young plantations against grazing (Selvam et al. 2003). We need to know where to concentrate restoration efforts in order not to unfairly restrict dependent communities, who can be wonderful advocates. It is possible to manage mangroves as multiple use systems for the high and sustainable yield of natural products, such as timber and charcoal production, and shrimp, but most such projects end in disaster as a result of poor short-term management practices (Field 1999, 48). Monitoring change over time with flexible data is key to long-term success. My methods can find areas for conservation and for restoration. Once areas are identified, buffer zones where the people s activities need to be controlled may be necessary areas. One suggestion is a 2000 m buffer zone from protected areas (Hossain 7

8 et al. 2009, 1089). This only works on large protected areas, but what about the small fragmented areas with people living in them? Wildlife conservationists and NGOs want to prohibit all human activities but many academics and people-oriented NGOs support communities living within protected forests (Hossain et al. 2009, 1090). Many people have been relocated, but perhaps, they do not have to be if mangrove management had the proper tools. Budget: Full-time salary for 1.5 people at $35/hour for $157, months Airfare/lodging for 3 months in Indonesia $15,500 Airfare/lodging for 1 month in Indonesia $6,500 Boat rentals/car rentals in Indonesia $4,800 Lab fee for software license: ArcGIS, ERDAS $15,250 IMAGINE 8.4 image processing package for Remote Sensing images 2 Rugged Laptops $5,700 Garmin Waterproof Handheld GPS Plus $700 FRS/GMRS Camera EPSON ACTION II scanner $359 Environmental Sampling equipment $1000 Office Supplies $350 Landsat images for all of Indonesia (187 $ 24,497 images) [ IKONOS images $10/ square km (selective use) $8,800 Total: $240,456 Timeframe: The initial project of delineating the extent and condition of the Langsa mangroves, along with identifying suitable sites for restoration efforts, will take approximately 18 months. However, monitoring success will be ongoing for the next 10 years. 1. (July) Pre-planning for trip and ordering supplies/equipment 2. (August-October) Obtain and process satellite images for current extents and conditions 3. (November-January) Three month field survey: a. Gather information, data, and maps from government departments b. Meet with local forestry experts and obtain guides 8

9 c. Take georeferenced photos using GPS-Photo Link to verify mangrove locations and conditions; ground control points for georeference d. Field trips to obtain first hand knowledge on local environment and vegetation coverage types e. Sampling stations for environmental variables 4. (February-April) Development of the Thematic layers of the model for Site Suitability Maps 5. (June-August) Map Production 6. (September-November) Results Write-Up and Publication, including website development 7. (December) (2 nd trip to Indonesia) Workshops with communities, Local Forestry Experts and NGOs on Suitable Restoration Sites 8. Every year, satellite images will be examined to assess the success of the planting programs. Bibliography Field, C.D. (1999) Mangrove Rehabilitation: Choice and Necessity. Hydrobiologia, 413, p Giri, C., Zhu, Z., Tieszen, L. L., Singh, A., Gillette, S., and Kelmelis, J. A. (2008) Mangrove Forest Distributions and Dynamics ( ) of the Tsunami-affected Region of Asia. Journal of Biogeography, 35, p Hossain, Zakir, Tripahti, Nitin K., and Gallardo, Wenresti G. (2009) Land Use Dynamics in a Marine Protected Area System in Lower Andaman Coast of Thailand, Journal of Coastal Research, 255, P Kairo, J.G., Kivyatu, B., and Koedam, N. (2002) Application of Remote Sensing and GIS in the Management of Mangrove Forests within and Adjacent to Kiunga Marine Protected Area, Lamu, Kenya. Environment, Development and Sustainability, 4, p Roy, Anjam Kumer Dev, Alam, Khorshed, and Gow, Jeff. (2012) A Review of the Role of Property Rights and Forest Policies in the Management of the Sundarbans Mangrove Forest in Bangladesh. Forest Policy and Economics, 15, p

10 Selvam, V., Ravichandran, K.K., Gnanappazham, L. and Navamuniyammal, M. (2003) Assessment of Community-Based Restoration of Pichavaram Mangrove Wetland Using Remote Sensing Data. Current Science, 6, p Spalding, M.D., Blasco, F. and Field, C.D. (eds.) (1997) World Mangrove Atlas. The International Society for Mangrove Ecosystems, Okinawa. Ximenes, Arimatea de Carvalho, and Scott, Philip Conrad. (2007) Selecting Suitable Sites for Red Mangrove Restoration Using GIS and Geoprocessing. Anais XIII Simposio Brasileiro de Sensoriamento Remoto, INPE, p