Spatio-temporal and structural analysis of vegetation dynamics of Lowveld Savanna in South Africa

Savanna vegetation structure parameters are important for assessing the biomes status under various disturbance scenarios. Despite free availability remote sensing data, the use of optical remote sensing data for savanna vegetation structure mapping is limited by sparse and heterogeneous distribution of vegetation canopy. Cloud and aerosol contamination lead to inconsistency in the availability of time series data necessary for continuous vegetation monitoring, especially in the tropics. Long- and medium wavelength microwave data such as synthetic aperture radar (SAR), with their low sensitivity to clouds and atmospheric aerosols, and high temporal and spatial resolution solves these problems. Studies utilising remote sensing data for vegetation monitoring on the other hand, lack quality reference data. This study explores the potential of high-resolution TLS-derived vegetation structure variables as reference to multi-temporal SAR datasets in savanna vegetation monitoring. The overall objectives of this study are: (i) to evaluate the potential of high-resolution TLS-data in extraction of savanna vegetation structure variables; (ii) to estimate landscape-wide aboveground biomass (AGB) and assess changes over four years using multi-temporal L-band SAR within a Lowveld savanna in Kruger National Park; and (iii) to assess interactions between C-band SAR with various savanna vegetation structure variables. Field inventories and TLS campaign were carried out in the wet and dry seasons of 2015 respectively, and provided reference data upon which AGB, CC and cover classes were modelled. L-band SAR modelled AGB was used for change analysis over 4 years, while multitemporal C-band SAR data was used to assess backscatter response to seasonal changes in CC and AGB abundant classes and cover classes. From the AGB change analysis, on average 36 ha of the study area (91 ha) experienced a loss in AGB above 5 t/ha over 4 years. A high backscatter intensity is observed on high abundance AGB, CC classes and large trees as opposed to low CC and AGB abundance classes and small trees. There is high response to all structure variables, with C-band VV showing best polarization in savanna vegetation mapping. Moisture availability in the wet season increases backscatter response from both canopy and background classes

Linking scales and disciplines : an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management

CITATION: Berger, C. et al. 2019. Linking scales and disciplines : an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management. Climatic Change, 156:139–150, doi:10.1007/s10584-019-02544-0.The original publication is available at https://www.springer.com/journal/10584Southern Africa is particularly sensitive to climate change, due to both ecological and socioeconomic factors, with rural land users among the most vulnerable groups. The provision of information to support climate-relevant decision-making requires an understanding of the projected impacts of change and complex feedbacks within the local ecosystems, as well as local demands on ecosystem services. In this paper, we address the limitation of current approaches for developing management relevant socio-ecological information on the projected impacts of climate change and human activities.We emphasise the need for linking disciplines and approaches by expounding the methodology followed in our two consecutive projects. These projects combine disciplines and levels of measurements from the leaf level (ecophysiology) to the local landscape level (flux measurements) and from the local household level (socio-economic surveys) to the regional level (remote sensing), feeding into a variety of models at multiple scales. Interdisciplinary, multi-scaled, and integrated socio-ecological approaches, as proposed here, are needed to compliment reductionist and linear, scalespecific approaches. Decision support systems are used to integrate and communicate the data and models to the local decision-makers.https://link.springer.com/article/10.1007/s10584-019-02544-0Publisher's versio

Age schedules of intra-provincial migration in Kenya

Background: Migration today is a complex process determined by inter-related historical, geographical, economic, sociological and political factors. There are linkages between life-course transitions and patterns of movement necessitating estimation of migration propensities by age, sex and other characteristics. However, analysis of age specific migration propensities has been limited in developing countries.Data source and methods: Data was derived from the 2009 Kenya population and housing Census via the IPUMS data Series. The standard 7-parameter age migration schedule due to Rogers and Castro (1981) was fitted using Microsoft excel workbook using solver.Results: Large volumes of movements occur between ages 17 and 24.The peak ages at migration are similar to those observed in Asian migration patterns. The age pattern for all the regions had two peaks contrary to the standard with four.Conclusion: The results suggest that the main contributory factors behind migration schedules are schooling, labour force and associational moves.

La reconnaissance de l'indépendance du Kosovo, un enjeu pour l'avenir du droit international et une légitimation pour certains actes commis par le passé

Master [120] en droit, Université catholique de Louvain, 201

Socio-Economic and Cultural Determinants of Human African Trypanosomiasis at the Kenya – Uganda Transboundary

<div><p>Background</p><p>Kenya and Uganda have reported different Human African Trypanosomiasis incidences in the past more than three decades, with the latter recording more cases. This cross-sectional study assessed the demographic characteristics, tsetse and trypanosomiasis control practices, socio-economic and cultural risk factors influencing <i>Trypanosoma brucei rhodesiense</i> (<i>T.b.r.</i>) infection in Teso and Busia Districts, Western Kenya and Tororo and Busia Districts, Southeast Uganda. A conceptual framework was postulated to explain interactions of various socio-economic, cultural and tsetse control factors that predispose individuals and populations to HAT.</p> <p>Methods</p><p>A cross-sectional household survey was conducted between April and October 2008. Four administrative districts reporting <i>T.b.r</i> and lying adjacent to each other at the international boundary of Kenya and Uganda were purposely selected. Household data collection was carried out in two villages that had experienced HAT and one other village that had no reported HAT case from 1977 to 2008 in each district. A structured questionnaire was administered to 384 randomly selected household heads or their representatives in each country. The percent of respondents giving a specific answer was reported. Secondary data was also obtained on socio-economic and political issues in both countries.</p> <p>Results</p><p>Inadequate knowledge on the disease cycle and intervention measures contributed considerable barriers to HAT, and more so in Uganda than in Kenya. Gender-associated socio-cultural practices greatly predisposed individuals to HAT. Pesticides-based crop husbandry in the 1970's reportedly reduced vector population while vegetation of coffee and banana's and livestock husbandry directly increased occurrence of HAT. Livestock husbandry practices in the villages were strong predictors of HAT incidence. The residents in Kenya (6.7%) applied chemoprophylaxis and chemotherapeutic controls against trypanosomiasis to a larger extent than Uganda (2.1%).</p> <p>Conclusion</p><p>Knowledge on tsetse and its control methods, culture, farming practice, demographic and socio-economic variables explained occurrence of HAT better than landscape features.</p> </div

Percent socio-cultural activities perceived to expose different gender to tsetse bites.

<p>Percent socio-cultural activities perceived to expose different gender to tsetse bites.</p

Respondents historical major crops' percentage cover in Kenya and Uganda.

<p>Respondents historical major crops' percentage cover in Kenya and Uganda.</p

Selected study villages in Kenya and Uganda transboundary.

<p>Selected study villages in Kenya and Uganda transboundary.</p

Comparison of recent methods used for tsetse and trypanosomiasis in Western Kenya and Southeast Uganda.

<p>Comparison of recent methods used for tsetse and trypanosomiasis in Western Kenya and Southeast Uganda.</p

Education levels education in Teso and Busia districts, Western Kenya and Busia and Tororo districts, Southeast Uganda.

<p>Education levels education in Teso and Busia districts, Western Kenya and Busia and Tororo districts, Southeast Uganda.</p