Rasmus Revermann

Evaluating the trade-off between food and timber resulting from the conversion of Miombo forests to agricultural land in Angola using multi-temporal Landsat data.

The repopulation of abandoned areas in Angola after 27years of civil war led to a fast and extensive expansion of agricultural fields to meet the rising food demand. Yet, the increase in crop production at the expense of natural resources carries an inherent potential for conflicts since the demand for timber and wood extraction are also supposed to rise. We use the concept of ecosystem services to evaluate the trade-off between food and woody biomass. Our study area is located in central Angola, in the highlands of the upper Okavango catchment. We used Landsat data (spatial resolution: 30×30m) with a bi-temporal and multi-seasonal change detection approach for five time steps between 1989 and 2013 to estimate the conversion area from woodland to agriculture. Overall accuracy is 95%, users accuracy varies from 89-95% and producers accuracy ranges between 92-99%. To quantify the trade-off between woody biomass and the amount of food, this information was combined with indicator values and we furthermore assessed biomass regrowth on fallows. Our results reveal a constant rise in agricultural expansion from 1989-2013 with the mean annual deforestation rate increasing from roughly 5300ha up to about 12,000ha. Overall, 5.6% of the forested areas were converted to agriculture, whereas the FAO states a national deforestation rate for Angola of 5% from 1990-2010 (FAO, 2010). In the last time step 961,000t per year of woodland were cleared to potentially produce 1240t per year of maize. Current global agro-economical projections forecast increasing pressure on tropical dry forests from large-scale agriculture schemes (Gasparri et al., 2015; Searchinger and Heimlich, 2015). Our study underlines the importance of considering subsistence-related change processes, which may contribute significantly to negative effects associated with deforestation and degradation of these forest ecosystems.

Tree Species Diversity and Composition of Miombo Woodlands in South-Central Angola: A Chronosequence of Forest Recovery after Shifting Cultivation

The study was carried out in the Cusseque area of the Municipality of Chitembo in south-central Angola. Our objectives were to assess the floristic diversity, the species composition, and stand structure of Miombo woodlands during regeneration after shifting cultivation. A total of 40 plots of 1000 m2 were surveyed and analyzed, corresponding to mature forests/woodlands and three fallow types of different age. The analyses were based on plot inventories of all trees with DBH ≥ 5 cm. A total of 51 woody species, 38 genera, and 19 families were recorded. The dominant family was Fabaceae, with subfamily Caesalpinioideae being very abundant. Shannon Diversity and Evenness were highest in mature forests and young fallows, while the mature forest stands showed the highest species richness. A Principal Coordinates Analysis (PCoA) showed many species shared between the intermediate fallow types, but only few species were shared with young fallows. Mature forests formed a clearly distinct group. This study shows potential pathways of forest recovery in terms of faster regeneration after agricultural abandonment and, thus, the results presented here can be used in future conservation and management plans in order to reduce the pressure on mature forests.

Impact of Shifting Cultivation on Dense Tropical Woodlands in Southeast Angola

Dry tropical woodlands are the characteristic natural vegetation type of southeast Angola, where precipitation is seasonal and nutrient content of soils is very low. Subsistence farmers overcome the poor abiotic conditions by using shifting cultivation based on specific site selection. We describe the vegetation composition of a previously unstudied dense woodland tract, which is preferred for agriculture, comparing it to nearby open woodland. We placed 25 vegetation plots randomly in the two types of natural woodland vegetation (open and dense) as well as in fallows with three different durations of land use. In each plot the cover of all vascular plant species was recorded. Variables describing vegetation structure, including basal area and leaf area index, were recorded. We analyzed soil samples for texture, pH, conductivity and nutrients, and investigated the impact of land use duration on woodland regeneration. We found that species composition and vegetation structure of the dense woodland differed greatly from the surrounding open woodland. The dense woodland is preferable for agriculture because of its higher soil content of silt, clay and potassium, higher woody biomass, and more abundant soft litter. A longer duration of land use affects species composition and diversity of the regenerating vegetation as well as nutrient content in the soil. In the near future, the increasing demand for agricultural land is likely to change the structure and composition of the dense woodland.

Linking Land Surface Phenology and Vegetation-Plot Databases to Model Terrestrial Plant α-Diversity of the Okavango Basin

In many parts of Africa, spatially-explicit information on plant α-diversity, i.e., the number of species in a given area, is missing as baseline information for spatial planning. We present an approach on how to combine vegetation-plot databases and remotely-sensed land surface phenology (LSP) metrics to predict plant α-diversity on a regional scale. We gathered data on plant α-diversity, measured as species density, from 999 vegetation plots sized 20 m × 50 m covering all major vegetation units of the Okavango basin in the countries of Angola, Namibia and Botswana. As predictor variables, we used MODIS LSP metrics averaged over 12 years (250-m spatial resolution) and three topographic attributes calculated from the SRTM digital elevation model. Furthermore, we tested whether additional climatic data could improve predictions. We tested three predictor subsets: (1) remote sensing variables; (2) climatic variables; and (3) all variables combined. We used two statistical modeling approaches, random forests and boosted regression trees, to predict vascular plant α-diversity. The resulting maps showed that the Miombo woodlands of the Angolan Central Plateau featured the highest diversity, and the lowest values were predicted for the thornbush savanna in the Okavango Delta area. Models built on the entire dataset exhibited the best performance followed by climate-only models and remote sensing-only models. However, models including climate data showed artifacts. In spite of lower model performance, models based only on LSP metrics produced the most realistic maps. Furthermore, they revealed local differences in plant diversity of the landscape mosaic that were blurred by homogenous belts as predicted by climate-based models. This study pinpoints the high potential of LSP metrics used in conjunction with biodiversity data derived from vegetation-plot databases to produce spatial information on a regional scale that is urgently needed for basic natural resource management applications.