The application of GIS and remote sensing on icipe s R&D paradigms

Transcription

1 KENYATTA UNIVERSITY GIS DAY 18 th Nov The application of GIS and remote sensing on icipe s R&D paradigms Elfatih M. Abdel-Rahman Gladys Mosomtai Tobias Landmann David Makori icipe-african Insect Science for Food and Health

2 icipe (Overview) More than 40 years, icipe (International Centre for Insect Physiology and Ecology) has been the principal insect and arthropod research institute for Africa. Research and development thrust for icipe s 4-H paradigm; - Plant health - Animal health - Human health - Environmental heath

3 icipe (Overview)

4 Earth Observation Unit within icipe It s part of the Adaptation to Climate Change and Ecosystems Services (ACCES) cluster Works closely with icipe scientists from all 4-H to provide geospatial services to various projects Involved in training students and staff from icipe as well as collaborating partner institutions on GIS and remote sensing.

5 Thematic areas for EOU Food Security - mapping of crops and cropping systems. Biodiversity (BD) indicators - Incidences of Ecosystem Failure, Habitat dynamics, Degradation. Integrated Land Use & Ecosystem Services - mapping the floral cycle to quantify pollination effects and understand bee health.

6 Thematic areas for EOU Disease mapping - involved in mapping the vector habitat as a proxy for disease occurrence (than mapping the disease itself)

7 Food security 1. As part of risks assessment for agriculture: Predicting the Impacts of climate change on future pest and disease outbreaks (e.g., Diamondback moth infestation in Taita) Current 2013 Future 2055 Methodology 1. Species distribution modelling tools e.g MAXENT, GARP 2. Data worldclim data Africlim data RS variables LST, NDVI Data source

8 Food security 2. Crop and cropping patterns mapping for pests and disease prediction (e.g., Stem borers in Maize) this work is under progress (First field visit November 2014)

9 Food security Multi-sensor time series remotely sensed data (RapidEgye, Landsat 8, Sentinel-2 and SAR) - Crop mask - Crop types (patterns) - Seasonality (phenology) - Surrounded areas - Use of machine-learning classification algorithms (e.g., Random forest)

10 Food security Developing java-based RF for agricultural land use classification tool The tool does automatic classification using random forest algorithm Red- set directory Green- upload image to classify Set parameter of the algorithm

11 Biodiversity (BD) indicators Land productivity decline mapping ( ): Approach Data MODIS NDVI MOD 13Q Product TRMM - rainfall data Sources Multi-sensor approach for human-induced land productivity mapping ( )* Software used 1. IDRISI 2. ENVI 3. ARCMAP *Landmann & Dubovyk (2014) Spatial analysis of human-induced vegetation productivity decline over eastern Africa using a decade ( ) of medium resolution MODIS time-series data. Int. J. Applied Earth Observation and Geoinformation 33: published paper

12 Biodiversity (BD) indicators Rainfall corrected (normalized) Normalized Difference Vegetation Index (NDVI) time-series data, at 250-meter resolution, is used to map human-induced change between 2000 and 2012 Moderate decline severe decline

13 Ecosystem services quantification Flower mapping using airborne hyperspectral data Knowledge about the floral cycle and the abundance and distribution of flowering of mellipherous plants in the landscape Bee hive productivity and bee health studies as well as pollination The study site is located in the Mwingi Central Sub County, Kitui County in Kenya AisaEAGLE imaging spectrometer (Feb 2013 and Jan. 2014)

14 Reflactance (%) Reflectancy (%) Ecosystem services quantification Reference data collection (Flowering trees, flowers color) Bee hive productivity and bee health studies as well as pollination Flowering, green trees and soil endmembers 60 Base Soil Flowering Trees Green Trees Green Trees Flowering Trees Base Soil Brown Trees Wavelength (nm) Wavelength (nm)

15 Ecosystem services quantification Spectral linear unmixing Change vector analysis (CVA)

16 Ecosystem services quantification Air- and space-borne imaging & in situ observations

17 Ecosystem services quantification Accuracy assessment Class Ground Truth Accuracy Data (%) Flowering in February 2013 Flowering in January 2014 Overall 70.95

18 Disease mapping Animal tracking to understand their migration routes in order to understand rift valley fever (RVF) outbreak Seasonal pattern of livestock, disease vectors and ecological variables enhanced our understanding of how, where and why certain diseases occur and spread At the end something like Income increase and food insecurity reduced through support to economic growth

19 Disease mapping Animal tracking to understand their migration routes in order to understand RVF outbreak

20 Factors that determine occurrence, vector trajectories, animal migration as qualitative and quantitative data linkages Livestock Land form Degradation Climate, weather Land dynamics - Land cover and land use - Vegetation seasonality - Population and stocking density - Fodder availability and quality - Flooding patterns - Water body density and distribution Constraints & Challenges: data availability, secondary or primary, feature behavior

21 Factors that determine occurrence, vector trajectories, animal migration as qualitative and quantitative data linkages Livestock Land form Degradation Climate, weather Land dynamics Cropland and irrigation expansion Vegetation production and trends Vegetation variables: amplitudes, point of greening Drought indicators Flooding regime Constraints & Challenges: data availability, secondary or primary, feature behavior

22 Current work in NE Kenya GPS positions and movements of two herds (orange and red) overlaid on a satellite derived map showing inundation patterns for 2012 near to permanently flooded 4-6 months of flooding /year < 4 months of flooding /year

23 Disease mapping Vector sampling sites, ecological variables and animal migration routes Vector diversity, animal serological sampling points and animal migration routes

24 Mapping land use dynamics and RVF in Baringo, Isiolo and Garisa districts Understanding dynamic drivers of disease is vital in understanding how to manage outbreaks, new niches that are developing and coming up with preventive measures According et al. (1992) and Linthicum et al. (2001) RVF occurs in: soil types-solonetz, solanchaks, planosols Elevation-less than 1100m asl Vector-Aedes,Culicine, and others In cycles of 5 to 15 years of heavy rainfall and flooding especially in arid and semi-arid low lying flat landscape areas with accumulation of flood water in depressions known as dambos and has been connected to El Niño/Southern Oscillation Dense vegetation cover persistent for 3 months NDVI greater than 0.1

25 Data used

26 Spread of RVF outbreak over the years

27 Mapping RVF niches using RS Case study of Somaliland According to Hightower et al solonetz, calcisols, solonchaks and planosols area associated with RFV because of its ability to retain water for long hence providing breeding ground for mosquitoes Garisa is largely covered by solonetz soil type -Hydro tools was used to generate the drainage pattern of the study area using 30m DEM -Rivers developed from 200,000m2 drainage area -Sinks indicate area where water can collect e.g dambos -Generated using Hammond landform formula -Garisa and Isiolo is largely flat -Baringo varies from plains with high mountains to nearly flat plains

28 Conclusions Successful applications of GIS/ RS (Spatial modeling) in addressing icipe s R&D themes The results/ outputs of these projects (e.g., land productivity and flower maps) could be use to assist researchers and policy makers in taking informed decisions regarding issues of food security that uplifting livelihood of people

29 Acknowledgements

30 Thank you