Foresttransitionsin mountainareas Research challenges and some examples

Transcription

1 Foresttransitionsin mountainareas Research challenges and some examples Presentationat the Thematic daysonforestsand climatechange November 9-10, Brussels. Anton Van Rompaey 1, Kim Chi Vu 2, Steven Vanockelen 1, Armando Molina 1, Karolien Vermeiren 1 1: Divisionof Geography, Departmentof Earthand EnvironmentalSciences, K.U.Leuven- Belgium 2: Faculty of Geography, Hanoi University of Science - Vietnam

2 Foresttransitions Transition from a stage net deforestion to a stage of net forest cover increase 1985 E.g. Valley of the Drome River (SE-France)

3 Forest transition in France. Mather et al., Forest transitions

4 Research challenges 1. Detection of forest transitions Forest transitions result in patchy landscapes with various stages of forest degradation and regeneration Forest transitions typically start in remote inaccessible places such as mountain areas Remote sensing seems to be a privileged tool methods suffer from methodological challenges in mountain area s: Geometric distortion due to topography Illumination and shade effects Processing of data for large areas with limited reference data Romanian Carpathians, 2011 True colour composites of forested area in the Romanian Carpathians with various levels of topographic correction, Vanonckelen et al. in press

5 Research challenges 2. Impact of forest transitions on ecosystem services? Hydrology Sediment fluxes Natural hazards Carbon sequestration Biodiversity Observed data residual 0 Time series of land use/land cover maps of the Pangorcatchment in the central part of Ecuador Molina et al. in press Year Monthly streamflow (mm)

6 Research challenges 3. Understanding drivers and controlling factors of forest transition Identified pathways of forest transition (Rudelet al., 2005): negative feedbacks of deforestation pushespeople away: e.g. wood scarcity, soil depletion, landsliding, flooding, regional/global economic development pulls people towards growth centres with higher quality of life: e.g. abandonment of mountain area s, rural > urban migrations Expected urban growth of Kampala (2030) Vermeiren et al., in press.

7 Research challenges Disentanglement of socioeconomic and biophysical controlling factors of forest transition Forest transitions are the result of decisions/behaviour of people Drivers and controlling factors of forest transition are in most cases location-specific Requires the understanding of the sometimes very subtle interactions between policy, the way-of-living and the biophysical environment Ecuadorian Andes, 2003

8 An example from NW-Vietnam Khang Dao Thai 5 km Ethnic groups in the catchment of the Suoi Muoi Son La province. Vu, K.C et al., in press. Kinh

9 Wayof living of ethnic groups? Thai household Khang household Kinh household 1 mi VND = 30 EUR

10 Wayof living of ethnicgroups

11 Vu, K.C., et al., in press.

12 April, 2010

13 Shifting cultivation: frequent transitions from upland field to shrubland and vice versa After 1996: gradual trend : upland field shrub forest Who is responsible for this forest transition? Spatial analysis necessary

14 Spatial analysis Mapping of shrub-upland field and upland field shrub conversion Calibration of logistic regression whereby following probabilities are assessed P(S UF) = probability that shrub is converted to upland field P(UF S) = probability that upland field is converted to shrub Explaining variables:

15 Calibrated coefficients of the MLR-model Intercept Dist to road Dist to village Elevation Slope gradient Lithology Lithology Lithology Lithology Lithology Ethnicity Ethnicity Ethnicity Controlling factors of upland field shrub conversions L1 schist L2 sandstone L3 limestone L4 basalt L5 shale E1 Thai E2 Kinh E3 Khang NS NS = non-significant at a 5% confidence level, Positive coefficients: positive correlation between predictor and P(UF S) Negative coefficients: negative correlation between predictor and P(UF S) NS P(UF S) preferentially on land units With steep slopes With high elevation With unfertile soils (schist or shale) On accessibleplaces (nearby the major road) Around Kinh villages (marketoriented, non-farming incomes)

16 Assessed UP S probabilities

17 How should we deal with the factor ethnicity way of living? The case study showed that: There is some multicollinearityin the dataset: e.gthe Kinhlive on the best accessible places, while the Khang live in very remote area s But: there is no physical determinism: even in the same biophysical/accessibility setting Kinh, Thai and Khangpeople will behave different! Probability assessments are therefore better if they include a factor ethnicity

18 Conclusions A universal model that explains the spatial and temporal variability of forest transitions worldwide does not exist In many cases forest transitions are the resultant of choices of individualsthat make decisions based on perceived opportunities These opportunities are steered by the biophysical environment, marketaccessibility, the policyframework, knowledge, way of living and traditions The development of a policy to promote forest transitions require an thorough understandingof all controlling factors and their interactions Human dimension is more difficult to quantify/model but is equally important