Integrating Participatory Methods and Remote Sensing to Enhance Understanding of Ecosystem Service Dynamics Across Scales
1.1. Mapping Ecosystem Services
“top down “technology-based” approaches (e.g., conventional geographic information systems (GIS) and remote sensing) when applied to indigenous territories may delegitimize Traditional Ecological Knowledge and, in extreme cases, may cause indigenous people to lose control over management of their natural resources” [38:94]
2. Materials and Methods
2.1. Study Site
2.2. Participatory Mapping
- Kopriay—historically more reliant on pastoralism with larger cattle herds, N = 1,094, 217 households;
- Ayepa—historically more reliant on flood-retreat agriculture, N = 1,275, 241 households;
- Napasmuria—largely poor households who have been subject to the wider regional conflict, generally resulting in their loss of animals and resettlement in a more urban context, and higher dependency on state resources such as safety net programs, N = 2,110, 418 households .
2.3. Remote Sensing
2.4. Deriving ES Metrics for Each Land Cover Type
2.5. Integrating Traditional Ecological Knowledge with Satellite Data to Map Ecosystem Services
3.1. Participatory Mapping
3.1.1. Annotated Maps
- Lake cultivation (hatched turquoise areas on Figure 4): at peak flood, the Omo River would fill large bodies of water between meanders of the river, in which cultivation would be carried out as it receded. Participants relayed that due to the loss of the flood, these are now only rainfed, but still defined as lakes by the community, in contrast to:
- Pond cultivation (grey-dotted areas on Figure 4): temporary rain-fed ponds that form during the wet-season, in which cultivation is carried out as it receded.
- Valley cultivation (turquoise lines on Figure 4): between Kopriay and the river there are valleys that can be cultivated during the wet season.
- Irrigation (pink areas on Figure 4): some irrigation is supported by the woreda near Kangaten, currently for cultivation of grass for fodder programs. The green dots represent historic irrigation sites, established by the Swedish Philadelphia Church Mission (SPCM) and active between the early 1970s until the early 2000s.
- Fishing, in the river and large lakes. Fishing in the river was traditionally a man’s task, with fish often consumed at the river immediately. According to an Ayepa woman “Why are you asking us about the fish? It’s the men’s work… Most of the time, we women do not eat the fish. Fish is for men. They eat there and if they like it they sell it there. If they wish to bring home, it’s according to their will; it’s not a must.”
- Hunting and trapping. Again, a gendered activity, with men hunting larger animals, often for cultural reasons as well as for food. Women and children trap smaller animals nearer to their settlements.
- Wild fruits, collected in the shrubland and forests around Ayepa and Napasmuria.
- Timber, fuel, and fiber, collected in the riverine forests.
- Water for livestock, accessed at ponds and lakes. The grazing ES map (Figure S1 in the supplementary material) also shows some waterholes for livestock by the river.
- Amokat, a salty soil, mixed with tobacco as a flavor enhancer and smoked.
3.1.2. Attribute Ecosystem Services to Land Covers and Rank Ecosystem Services
“Water is life…. Water is also future. The animals also drink water. The grains also use water. The wild food/fruit also use water. The sun also needs water. When the rain rains, the sun comes out. The bees also drink water, that’s why it is producing the honey. The materials for building house and firewood, even they require water. The fish is also drinking water and taking shower in the water. Wildlife also use water for drinking. The cattle also drink water. After drinking water, they produce milk and butter and meat, produce skin for sleeping.”
“Because honey is not available at every time. Also, honey cannot be eaten like food. You have to eat it slowly with other foods.” [Ayepa man].
“Honey is used to sell and buy cattle and goats. Honey is also used for alcohol.” [Kopriay woman].
3.1.3. Identify Trajectories of Change
“We need pumps for irrigation, taps for drinking water, food, as well as new school construction”.
3.1.4. Changing Cultivation
3.2. Land Cover Mapping
3.3. ES Metrics
3.4. Integrated ES Maps
4.1. ES Change in the Lower Omo
4.2. Methodological Contributions to the Literature
4.2.1. Limitations and Areas for Further Work
- Limitations of participatory mapping: Challenges with time availability, literacy levels of participants, and language barriers meant we had to simplify the classifications for ES importance (using high/medium/low/none, instead of an ideal scale from 0 to 10), and using prompts regarding how often the service is used or whether the household depends on it daily, seasonally or for special occasions. An observation from the importance classifications recorded in Table S1 for each focus group compared to the tile ranking was that when ranking was not enforced (i.e., in the original mapping activity with classifications of high/medium/low/none), higher values were prescribed to a broader range of services. We hypothesize this is because of the broad range of services being utilized in the present day, compared to pre-Gibe III, when elements like wild fruits and fish were more seasonal and not relied on as heavily. The consistently high classifications made it difficult to distinguish between ES, so for the ES metrics analysis importance classifications from the mapping activity (as recorded in Table S1 for each focus group) were not used in the analysis and the tile rankings were. Future work is needed to help us to find a more detailed way of quantifying importance.Additionally, further work is required on the methodology for mapping cultural and regulating ES. The absence of these from the results does not mean there was no articulated value of these ES, but that our approach to the participatory mapping elicited either very specific (i.e. individual trees that were hard to identify on the map) or very broad (i.e., a service provided by the whole territory, such as biodiversity) extents which participants were hesitant to map (as seen in Table S5). Given the known limitations of producing satellite measurements of more subjective values related to societal wellbeing and cultural perceptions , this remains an important area to address within the participatory mapping. Further work is also needed to elicit better probes for regulating services, given these were the least mapped.
- Limitations of satellite mapping: Given the relatively small enclosures and highly variable cultivation and rainfall seasonality, it was difficult to map croplands. Instead, croplands would have been incorporated into bare land, grassland, or shrubland. Future work could address this by digitizing cropland in high resolution aerial imagery or using new satellite sensors (e.g., Senintel-2) which offer improved capabilities for mapping cropland, i.e., high spatial, temporal and spectral resolution. This would help with challenges such as the valley cultivation around Kopriay, which are likely areas of grassland or shrubland that have been cleared to grow crops, thus potentially being classed as bare ground in the 2016–2019 maps, as our land cover classification was unable to discriminate between these. Indeed, Kopriay participants indicated these were important areas for cultivation. Additionally, this limitation meant we were not able to map and quantify the loss of flood-retreat agriculture (one of the main changes reported in focus groups). In a future paper, we will combine flood-retreat cultivation yields from survey data with timeseries of flood extent from satellite imagery to estimate loss of riverine crop production.Our approach was also limited by the assumption that each landcover type has a fixed ES capacity. While many studies also take this approach [16,83], in reality, there will be considerable spatial heterogeneity within classes as well as variation over time. For example, the capacity of shrubland to provide services will vary depending on factors such as percentage shrub cover and Net Primary Productivity (NPP). To overcome this, some ESS assessments combine land cover data with other satellite products. Thus, future work should explore the integration of remotely-sensed biophysically variables (such as NPP estimated from NDVI imagery ) with participatory methods to improve representation of spatio-temporal variability in ES capacity at the landscape level.
- Limitations of integration: Both past and present weightings need to be considered for future work. For example, we collected ES capacity and value data about the present, and lakes had both a relatively high ES capacity and value. However, the loss of lakes is likely to have a higher impact than suggested by our method because lakes previously had higher capacities, particularly when compared to ponds. Similarly, the river was given a modest capacity score that would be significantly higher pre-Gibe III, which would influence the weighting of both the river and associated cropland.
Conflicts of Interest
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|Community||Water||Crops||Grazing Livestock||Wild Fruits||Fish||TFF||Bush Meat||Shade||Salt||Honey|
|Grazing—number of livestock supported||⇗||⇗||⇘||⇗||⇗|
|Timber, Fuel, and Fiber||⇘||⇘||⇘|
|Increasing 1||Schools||People||Schools||Ceremonial sites||Schools||Donkeys|
|Increasing 2||Cattle numbers||Cattle numbers||Ceremonial sites||Schools||Sheep & goat numbers||Pond cultivation|
|Increasing 3||Salt||Villages||Grazing||n/a||Pond cultivation||Villages|
|Decreasing 1||River cultivation||River cultivation||River cultivation||River cultivation||River cultivation||River cultivation|
|Decreasing 2||Bush meat||Lake cultivation||Lake cultivation||Cattle||Irrigation||Irrigation|
|Decreasing 3||Grazing/ water||Grazing||Pond cultivation||Grazing||Lake cultivation||Lake cultivation|
|K-W||River > Lake > Valley||Valleys > Lake|
|K-M||River > Lake > Valley > Ponds||Lake > Valley > Ponds|
|A-W||River > Lake > Irrigation > Pond||Pond > Irrigation > Lake|
|A-M||River > Irrigation > Pond > Lake||Pond > Lake > Irrigation|
|N-W||River > Lake > Irrigation > Pond||Lake > Pond > Irrigation|
|N-M||River > Lake > Irrigation > Pond||Pond > Lake > Irrigation|
|Land Cover||ES Sub-Categories Supporteda||Mean Number of ES||Mean Number of ES Sub-Categories||Mean ES Capacity||Mean ES Value|
|Shrubland||Livestock (3), Wild Fruits (3), Bushmeat (3), TFF (2), Crops (1), Honey (1), Shade (1)||9.33||4.67||0.95||0.83|
|Ponds||Water (3), Crops (3), Livestock (2), Shade (1), Wild Fruits (1), TFF (1), Honey (1), Bushmeat (1)||6.33||4.33||0.70||0.78|
|River||Fish (3), Water (3), Livestock (1), Bushmeat (1)||4.67||2.67||0.58||0.66|
|Lakes||Crops (3), Fish (2), Livestock (2), Wild Fruits (1), Water (1), Honey (1), Bushmeat (1), TFF (1)||5.67||4.00||0.61||0.65|
|Forest||Wild Fruits (3), Wood (3), Bushmeat (2), Honey (2), Livestock (1)||5.00||3.67||0.77||0.57|
|Grassland||Livestock (3), Crops (1), Bushmeat (1), TFF (1)||4.67||2.00||0.36||0.46|
|Bare ground and Urban||None||0||0||0||0|
|Number of Services||No. of ES Sub-Categories||ES Capacity||ES Value|
|Kebele||00–03||16–19||% change||00–03||16–19||% change||00–03||16–19||% change||00–03||16–19||% change|
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Hodbod, J.; Tebbs, E.; Chan, K.; Sharma, S. Integrating Participatory Methods and Remote Sensing to Enhance Understanding of Ecosystem Service Dynamics Across Scales. Land 2019, 8, 132. https://doi.org/10.3390/land8090132
Hodbod J, Tebbs E, Chan K, Sharma S. Integrating Participatory Methods and Remote Sensing to Enhance Understanding of Ecosystem Service Dynamics Across Scales. Land. 2019; 8(9):132. https://doi.org/10.3390/land8090132Chicago/Turabian Style
Hodbod, Jennifer, Emma Tebbs, Kristofer Chan, and Shubhechchha Sharma. 2019. "Integrating Participatory Methods and Remote Sensing to Enhance Understanding of Ecosystem Service Dynamics Across Scales" Land 8, no. 9: 132. https://doi.org/10.3390/land8090132