Detecting Vegetation Change in Response to Confining Elephants in Forests Using MODIS Time-Series and BFAST

Jacqueline Morrison,Elias Symeonakis,Bradley Cain,Fred Omengo,Martin Jones,Thomas Higginbottom,Susan Walker

doi:10.3390/rs10071075

Jacqueline Morrison, Elias Symeonakis + Show 5 more

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https://doi.org/10.3390/rs10071075

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Abstract

Afromontane forests are biodiversity hotspots and provide essential ecosystem services. However, they are under pressure as a result of an expanding human population and the impact of climate change. In many instances electric fencing has become a necessary management strategy to protect forest integrity and reduce human-wildlife conflict. The impact of confining hitherto migratory elephant populations within forests remains unknown, and monitoring largely inaccessible areas is challenging. We explore the application of remote sensing to monitor the impact of confinement, employing the Breaks For Additive Season and Trend (BFAST) time-series decomposition method over a 15-year period on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) datasets for two Kenyan forests. Results indicated that BFAST was able to identify disturbances from anthropogenic, fire and elephant damage. Sequential monitoring enabled the detection of gradual changes in the forest canopy, with degradation and regeneration being observed in both sites. Annual rates of forest loss in both areas were significantly lower than reported in other studies on Afromontane forests, suggesting that installing fences has reduced land-use conversion from human-related disturbances. Negative changes in EVI were predominantly gradual degradation rather than large-scale, abrupt clearings of the forest. Results presented here demonstrate that BFAST can be used to monitor biotic and abiotic drivers of change in Afromontane forests.

Highlights

Over 31% of the Earths’ surface is covered by forests, and maintaining the integrity of forest ecosystems is a global conservation concern [1]
We explore the application of remote sensing to monitor the impact of confinement, employing the Breaks For Additive Season and Trend (BFAST) time-series decomposition method over a 15-year period on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) datasets for two Kenyan forests
Figure o2f. cChhanagnegienmthaepMsOfoDrISthEenhAabnecreddaVreegNetaattiioonnIanldPeaxr(kEVfrIo) mder2iv0e0d5–u2s0in1g5BsrheoakwsiFnogr:A(ad)dTithivee mSeaagsonnitude of changeainndthTreeMndO(BDFIASSETn);h(ba)nTcheedyVeeargtehteatbiroenakIpnodienxt w(EaVs dI)etdecetreidvebdy BuFsAinSgT;B(rce) aAkrseaFsoorf AfirdeddiettievceteSdebayson and Trend;IS(bM) CTDh4e5y1Aeaprrtohdeucbt.reakpoint was detected by BFAST; (c) Areas of fire detected by the MODIS MCD451A product

Summary

Introduction

Over 31% of the Earths’ surface is covered by forests, and maintaining the integrity of forest ecosystems is a global conservation concern [1]. Namely the Aberdare National Park (ANP), Mount Kenya, Mau Complex, Cherangani Hills and Mount Elgon, are known as the “Water Towers”; they are an important water resource for the country [4], accounting for 75% of the renewable surface water [10]. Two of these “Water Towers” (Aberdare mountain range and Mount Kenya) produce 55% of Kenya’s electricity, which is generated by hydropower, and support major irrigation schemes required by the agricultural sector that contributes to a quarter of the country’s GDP [11]. The degradation of Kenya’s forests and subsequent impact on climate change could severely affect agricultural production [12,13] and lead to negative ramifications for the human population

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