PPPPPPP66OPP5P6P - eoscience.esa.inteoscience.esa.int/landtraining2017/files/posters/JOHNSON.pdf ·...
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•Vnvestigate the land cover and land use change in semi?arid areas of 5ast 6frica and the impact that this change is having on biodiversity and ecosystem servicesJ •Vnvestigate innovative methodological approaches = assess the potential of combining bioacoustic monitoring1soundscape ecology techniques with remote sensing to carry out biodiversity assessments rapidly over arger spatial and temporal scalesJ •Provide information to decision?makers on the impact and sustainability of particular land use strategies in these regionsJ sdasd The first NcurrentH research question of the project= What are the trajectories of land cover changeB #PP and G6#PP in the 6S6Os of Uaringo2 Trajectories of land use change in the 6S6Os of 5ast 6frica Sarah Louise Johnson Sarah Oouise Fohnson NRst year Ph4 studentH Oondon #5R9 4TP~ 'ings 9ollege Oondon W Zoological Society of OondonB 96UV sarahJjohnson7kclJacJuk Kenya Ethiopia Tanzania Somalia Sudan Eritrea 6rid and Semi?6rid Oands N6S6OsH Prone to extreme drought events and food insecurityJ RapidB multi?dimensional land? use change and land degradationJ Uiological W cultural diversityJ +ain economic production systems 0 pastoralismB agro?pastoralismB irrigated agricultureJ ~ xDj of land mass 5ast 6fricaJ References #ext steps and future Work 25km • GaberlB GJ et alJB CDD@J Human-Appropriation-of-Net-Primary-ProductionJ Vnternet 5ncyclopaedia of 5cological 5conomicsJ • +J WegmannB UJ et alJ NCDRZHB Remote-Sensing-and-GIS-for-Ecologists:-Using-Open-Source-SoftwareB Pelagic PublishingB 5xeterB U'J • 'iageB OJ+J et alJB CDD@J Recent-landxcover.use-change-associated-with-land-degradation-in-the- Lake-Baringo-catchmentb-Kenyab-East-Africa:-evidence-from-Landsat-TM-and-ETM-+J Vnternational Fournal of Remote SensingB CxNRMHB ppJKCx&–K;DM • 3oogle 5arth 5ngine= https=11earthengineJgoogleJcom1 • 4VV 0 4ifference Vegetation ? one of the very first VVs • #4VV 0 #ormalised 4ifference ? often usedB values deviate when vegetation is very scarce1denseJ • S6VV 0 Soil?6djusted VV ? 6llows adjustment for effect of soil backgroundJ • +S6VVC 0 +odified S6VVC ? can provide better results in sparsely or very densely vegetated areasJ –ig xJ Vegetation indices N#4VVB S6VVB +S6VVCBW 4VVH calculated and compared for the study area Standard deviation NsJdH calculated across the #4VVB 4VVB S6VV and +S6VVC pixelsJ Gigher discrepancies Nhigher sJd valuesH are observed for ,greener, areas south of the lakeJ –ig MJ Spatial correlation of #4VVs NRRxRRpxB Spearman,s correlation coefficientH –ig RDJ +ap of sJd across the K indicesB showing where these indices differJ Study area= Uaringo countyB 'enya • +irrors land cover changes seen across the regionJ • Over MDj classified as arid or semi?arid landJ • 5nvironmental resources play a critical role in supporting livelihoodsJ • Severe pressure on natural resourcesB frequent droughts W food insecurityJ • Uaringo has some of the most seriously degraded land in 'enyaB so the effects of the land cover changes are acuteJ • 9urrently undergoing rapid change as part of national and regional development drivesJ N'iageB OJ+J et alJB CDD@H –igJR= Oake Uaringo sub?survey area 25km –ig ZJ #4VV map for UaringoB +arch CDR@B produced using SC imagery and 3oogle 5arth 5ngine N355HJ The higher the photosynthetic valuesB the darker the greenJ –ig @J #4VV thresholds map for UaringoB +arch CDR@B produced using SC imagery and 355J #4VV values range from ?R to LR and vegetation generally range from DJ; to DJxB with the larger values representing ,greener, surfacesJ Uare soils range from about DJC ? DJ;J The majority of Uaringo has a #4VV value of DJD ? DJ;B which indicate low vegetation activity and #PPJ These values could reflect the lower than average ,short rains, and subsequent drought conditions within the region in CDR@J Time series analysis will be carried out to examine seasonal and longer temporal trendsJ 6rid and Semi?6rid Oands N6S6OSH in 5ast 6frica are undergoing a period of rapid land?use changeB at least partly driven by change in land tenure systems and government?promoted shifts in livelihood forms from pastoralism to sedentarismB as well as investments in irrigated agricultureJ fg There is concern that these changes may exacerbate food insecurity and lead to the erosion of rural livelihoods as well as the loss of biodiversityB and the ability of the ecosystems to deliver services critical to human well?being N–6O CDRR~ 6very CDRCH ? but also lack of knowledge on how land change is affecting biodiversityB ecosystems and livelihoodsJ hfghf This project is examining the changes taking placeB to provide a holistic understanding of how land cover and land use change is affecting biodiversity and the provision of ecosystem services N5SHJ #et Primary Production N#PPHB represents the primary food energy source for the ecosystems in 5ast 6fricaB as elsewhereJ Guman appropriation of net primary productivity NG6#PPH is the aggregate impact of land use on #PP 1 biomass available each year in ecosystems and will be used as a gauge of land use intensityJ Vntroduction ? the issue in the 6S6Os This project is using 5arth Observation technologyB ecological measurementsB bioacoustic monitoring techniques and social surveysB to investigate change in semi?arid systemsJ –ig CJ conceptual framework for research objectives 4ense time series of remotely?sensed dataB and field?based measurements will quantify and map changes in land coverB #et Primary Production N#PPH and Guman appropriation of #PP NG6#PPHB and examine the trade?offs between human appropriation of #PPB biodiversityB and ecosystem multi? functionality NOefcheck et alJ CDR&~ 6llan et alJ CDRKHJ This project is also using the novel technique of bioacoustic monitoringB to assess the extent to which changes in biodiversity and community structure are detectable using this approachB and thus the potential of combining soundscape ecology techniquesJ 'ey +ethods The typical spectrum of healthy vegetation 0 very low reflectance in the red region and very high reflectance in the #VR regionJ hfghfgh The red and #VR part of the spectrum are important for mapping vegetationB and vegetation signatures are often used as proxy for photosynthetic health and vegetation activity NWegmann et alJCDRZHJ ghgh 4erived vegetation indicies NVVsH are linked to vegetation coverB biomass and #PP NPettoreli B CDR;HJ 4ifferent VVs existB and are often correlated but there are differences in what is depicted~ choosing an index depends on the ecological and landscape features of interestJ jghjgh Step R for ObjJR 0 investigate which existing VV is most suitable for mapping #PP in 6S6O regionsJ #PP maps will form a basis for G6#PP mappingJ –our Vegetation indices have so far been calculatedB mapped and compared for a N+arch CDR@H pre?processedB composite Sentinel C image of a sample of the study areaB using the cloud?based 3oogle 5arth 5ngine and RToolbox.spectralindices-B-Raster.corLocal-in R & hgjghjgh 4ifferences between VVs was quantified through the calculation of the standard deviation across the four different indicies N–ig RDH and Spatial correlation analysisB using Raster1corOocal in R N–ig MHJ corOocal analyses the correlation within moving local neighbourhoods between two rasters at a timeJ +ethod ? Objective R 1 Step R Vnvestigating vegetation index mapping as a proxy for #PPB using 3oogle 5arth 5ngine and RJ #PP W G6#PP ? land use intensity • #et Primary Production N#PPH 0 a measure of plant productivityB and represents (R food energy source for ecosystems in 5J6frica • Guman appropriation of #PP NG6#PPH 0 the aggregate impact of land use on #PP 1 biomass available each year in ecosystemsJ • The definition of G6#PP used for this research 0 G6#PP 0 #PPD ? #PPt G6#PP= • 9hanges the amount of #PPB trophic energyB remaining in the ecosystem for other speciesB can alter levels of biodiversity and energy flows within food webs and provision of ecosystem services NNVmhoffB UounouaRB et alJ CDDKHJ • 6 measure of the impact of human activities on productivity of natural ecosystems and of the human contribution to degradationJ • Understanding the spatial patterns of G6#PP can be extremely useful for monitoring areas experiencing ecosystem impactsJ –ig KJ calculation of G6#PP NGaberl et alJ CDD@H 4rivers of Guman 6ppropriation of #et Primary productivity NG6#PPH #et Primary Production N#PPH W flow of energy available Potential impacts of imbalance between #PP W G6#PP –ig ;J #PPB land use W G6#PP Research Objectives VV comparison suggests that #4VV and S6VV provide the most similar result B despite the soil adjustment of S6VVJ –urther investigation required of the appropriate ,O, value for S6VV N,O,H depends on soil background colourHJ (L = 0.5) s.d 6ll the VV values were pair?wise correlatedJ The maps show that the 4VV values had lowest correlation with all the other VVs~ #4VV had a generally high local correlation with S6VV and +S6VVCJ •–urther investigation of suitable VVs for #PP mapping in 6S6O areas •Time series analysis of #PP using remotely?sensed dataJ sdss •4esktop land cover classification and ground truthing study to create land cover maps and validate #PP maps asdasd •9ollect biomass and household survey data to create G6#PP maps Soundscape mapping 5cological measurements Results= from spectral to ecological information NDVI zones > 0.5 0.3 - 0.5 0.0 - 0.3 < 0.0 (Sueur et al 2008) (Charlesjsharp, CC BY-SA 4.0, Wikimedia Commons)
Transcript of PPPPPPP66OPP5P6P - eoscience.esa.inteoscience.esa.int/landtraining2017/files/posters/JOHNSON.pdf ·...
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