Mika Siljander

Airborne remote sensing of spatiotemporal change (1955-2004) in indigenous and exotic forest cover in the Taita Hills, Kenya.

We studied changes in area and species composition of six indigenous forest fragments in the Taita Hills, Kenya using 1955 and 1995 aerial photography with 2004 airborne digital camera mosaics. The study area is part of Eastern Arc Mountains, a global biodiversity hot spot that boasts an outstanding diversity of flora and fauna and a high level of endemism. While a total of 260 ha (50%) of indigenous tropical cloud forest was lost to agriculture and bushland between 1955 and 2004, large-scale planting of exotic pines, eucalyptus, grevillea, black wattle and cypress on barren land during the same period resulted in a balanced total forest area. In the Taita Hills, like in other Afrotropical forests, indigenous forest loss may adversely affect ecosystem services.

Predictive fire occurrence modelling to improve burned area estimation at a regional scale: A case study in East Caprivi, Namibia

Fires threaten human lives, property and natural resources in Southern African savannas. Due to warming climate, fire occurrence may increase and fires become more intense. It is crucial, therefore, to understand the complexity of spatiotemporal and probabilistic characteristics of fires. This study scrutinizes spatiotemporal characteristics of fires and the role played by abiotic, biotic and anthropogenic factors for fire probability modelling in a semiarid Southern African savanna environment. The MODIS fire products: fire hot spots (MOD14A2 and MYD14A2) and burned area product MODIS (MCD45A1), and GIS derived data were used in analysis. Fire hot spots occurrence was first analysed, and spatial autocorrelation for fires investigated, using Morans I correlograms. Fire probability models were created using generalized linear models (GLMs). Separate models were produced for abiotic, biotic, anthropogenic and combined factors and an autocovariate variable was tested for model improvement. The hierarchical partitioning method was used to determine independent effects of explanatory variables. The discriminating ability of models was evaluated using area under the curve (AUC) from the receiver operating characteristic (ROC) plot. The results showed that 19.2–24.4% of East Caprivi burned when detected using MODIS hot spots fire data and these fires were strongly spatially autocorrelated. Therefore, the autocovariate variable significantly improved fire probability models when added to them. For autologistic models, i.e. models accounting for spatial autocorrelation, discrimination was good to excellent (AUC 0.858–0.942). For models not counting spatial autocorrelation, prediction success was poor to moderate (AUC 0.542–0.745). The results of this study clearly showed that spatial autocorrelation has to be taken in to account in the fire probability model building process when using remotely sensed and GIS derived data. This study also showed that fire probability models accounting for spatial autocorrelation proved to be superior in regional scale burned area estimation when compared with MODIS burned area product (MCD45A1).

Agricultural Expansion and Its Consequences in the Taita Hills, Kenya

Abstract The indigenous cloud forests in the Taita Hills have suffered substantial degradation for several centuries due to agricultural expansion. Additionally, climate change imposes an imminent threat for local economy and environmental sustainability. In such circumstances, elaborating tools to conciliate socioeconomic growth and natural resources conservation is an enormous challenge. This chapter describes applications of remote sensing and geographic information systems for assessing land-cover changes in the Taita Hills and its surrounding lowlands. Furthermore, it provides an overall assessment on the consequences of land-cover changes to water resources, biodiversity and livelihoods. The analyses presented in this study were undertaken at multiple spatial scales, using field data, airborne digital images and satellite imagery. Furthermore, a modelling framework was designed to delineate agricultural expansion projections and evaluate the future impacts of agriculture on soil erosion and irrigation water demand.

Species diversity and geographical distribution of Scopulini moths (Lepidoptera : Geometridae, Sterrhinae) on a world-wide scale

This study draws together information on several aspects of species diversity in Scopulini (Lepidoptera: Geometridae, Sterrhinae) on a world-wide scale. Eight variables, describing for example rates of species description, synonymy rates, and geographical distribution of the Scopulini and its constituent genera, were coded for all putatively valid species of Scopulini and were incorporated into a computerized database. The dynamics of species descriptions per decade shows that it peaked around 1900, remained high until the 1940s, and the cumulative curve is yet to reach an asymptote. Both absolute and cumulative curves of species descriptions per decade have taken a variety of forms in the different biogeographical regions. Amongst biogeographical regions the number of synonyms per species is broadly related, that is, the more valid names, the more synonyms. The distributions of the total number of synonyms associated with valid species names and the numbers of authors describing different numbers of species are both right-skewed. Based on the distribution of the type localities of the putatively valid species, the Scopulini are a cosmopolitan group. The biogeographical regions where most species have been described from were found to be the African, especially sub-Saharan areas, and the Orient, whereas the low number of described species of the Neotropics is noticeable. The number of described species in relation to latitude follows roughly the shape of a normal distribution, being highest at low latitudes with a peak in temperate zones in the Northern hemisphere, and decreasing towards higher latitudes. The underlying causes for patterns of species descriptions are discussed, and the results of our analyses are in part used to estimate actual numbers of the Scopulini species occurring in each geographical area.

Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya

Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600–2200 m. The field measurements were carried out in 215 plots in 2013–2015 and ALS flights conducted in 2014–2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R2 = 0.88, RMSE = 52.9 Mg ha−1). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region.

Views from two mountains: exploring climate change impacts on traditional farming communities of Eastern Africa highlands through participatory scenarios

African mountains are characterized by high levels of biodiversity and provide ecosystem services to millions of people. Due to steep environmental gradients, growing human populations and geographical isolation, these coupled socio-ecological systems are highly vulnerable to climate change impacts. The capacity of local stakeholders to anticipate future changes and to assess their potential impacts is paramount for enhancing adaptation and resilience. Here we apply a participatory scenario development framework in two parts of the Eastern Afromontane Biodiversity Hotspot: Taita Hills in Kenya and Jimma rural area in Ethiopia. In each area, we facilitated local stakeholders in envisioning adaptation scenarios under projected climate changes by mid-21st century, and assessed the potential impacts of these pathways on land use and land cover. In the Taita Hills, under a business-as-usual scenario, human population and activities concentrate at high elevation, triggering cascade effects on remnant forest cover, biodiversity and ecosystem services. Alternative adaptation scenarios envisage reforestation associated with either improved agricultural practices or ecosystem restoration. In the Jimma area, rising temperatures are expected to disrupt traditional coffee production under a business-as-usual scenario, resulting in the loss of coffee-forest canopies and reduction of forest-dependent biodiversity. Alternative adaptation scenarios envisage either expansion of commercial coffee plantations or expansion of agroforestry, including traditional coffee farming. In the both Taita and Jimma, adaptation pathways present trade-offs between provisioning, supporting and regulating services, and between livelihoods and biodiversity conservation. Our findings encourage the use of multidisciplinary, bottom-up approaches for developing locally tailored, climate-smart and sustainable adaptation pathways.

Tree species discrimination using narrow bands and vegetation indices from airborne AISA Eagle VNIR data in the Taita Hills, Kenya.

Tree species inventory and mapping are important for the management and conservation of forests. Especially in tropical forests, field based inventories are very tedious and time consuming. Therefore, the crown-level spectral data collected by the high spatial resolution airborne imaging spectroscopy provides promising possibilities for improving the accuracy and efficiency of tree species inventory and mapping. In this study, the feasibility of AISA Eagle VNIR data for spectral discrimination of indigenous and exotic tree species in the Ngangao forest in the Taita Hills in south-eastern Kenya was examined. The airborne AISA Eagle VNIR data (400-876 nm, bandwidth approximately 4.6 nm) was acquired in January 2013. The data was georeferenced and atmospherically corrected with a final spatial resolution of 1 m. The field data consisted of 152 samples from 10 species (six indigenous and four exotic species), which were mapped both in the field and from the AISA images. Stepwise Discriminant Analysis was used for tree species classification using three sets of inputs: (1) all narrowbands, (2) a combination of narrowbands and selected vegetation indices (VIs), and (3) simulated blue, green, red and NIR broadbands. According to the results, both the narrowbands and VIs provided a cross-validated overall accuracy of 77.0%. The simulated broadbands provided considerably lower overall accuracy of 38.2%, which emphasizes the utility of hyperspectral data in tropical tree species discrimination. High overall accuracy (92.8%) was attained when separating only exotic and indigenous species.