Mapping Elephant Distribution with Remote Sensing and GIS

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Understanding the drivers of elephant abundance and distribution, both in Asia and Africa, is crucial for efficient elephant and landscape conservation since both species are running out of living space. Their enclosure in reserves in order to avoid elephant-human conflicts and the related changes in population led to significant issues regarding living space as well as habitat degradation due to unsustainable proportions. A thorough understanding of their migratory patterns is thus a prerequisite for successful management and conservation of elephant populations as well as habitat health. As long-ranging vertebrates, elephants play an essential role in ecosystems and, nowadays, the local economy due to tourism. However, reported increases in populations and their long-ranging activity has a significant effect on vegetation abundance and composition, other species biodiversity and ecosystem productivity. Spatio-temporal information regarding migratory patterns is thus crucial for conservation management to secure both sides of the coin: elephants and habitats. A variety of applications using GIS, remote sensing and GPS tracking of individuals per collars have advanced in the last years and are increasingly used in this context.

Spatial information at the landscape scale provides crucial information about the elephants’ ranging behaviour as a result of precipitation-driven dynamics of vegetation [1]. Mapping of elephant habitats, corridors and vegetation patterns has been successfully applied in this context using normalised difference vegetation index time series (NDVI) in combination with digital elevation models (DEM) derived from Landsat satellites. Especially Landsat 8’s Operational Land Imager (OLI) datasets from the USGS Earth Explorer provided valuable information regarding wet as well as dry seasons [2]. However, a variety of aspects need to be considered when using satellite imagery to map elephant habitat maps. Not only vegetation composition and abundance plays a critical role but in particular their temporal changes in seasonal terms and under current climate change issues. Furthermore, mapping of total biomass, forest canopy sizes and thicket patches in relation to time is essential to provide robust baseline information, as also is the identification of waterholes and potential refugia sites [3]. Remote sensing delivers the tool for a cost-effective provision of the baseline prerequisites when considering the spatial dimensions of elephant habitats and the remoteness of their location since sufficient funding for conservation is often more than rare.

Figure 1: The Marsabit study area and recorded elephant movements. (a) with agricultural areas (shaded and outlined in red), major roads (bold lines), minor roads (thin lines), and elephant locations recorded in (b) December 2005–2008 on a background of NDVI measured between December 19 and 31 December 2006, and (c) July 2006–2008 on a background of NDVI measured between 12 and 28 July 2006, draped over a digital elevation model. [1]

Figure 1: The Marsabit study area and recorded elephant movements. (a) with agricultural areas (shaded and outlined in red), major roads (bold lines), minor roads (thin lines), and elephant locations recorded in (b) December 2005–2008 on a background of NDVI measured between December 19 and 31 December 2006, and (c) July 2006–2008 on a background of NDVI measured between 12 and 28 July 2006, draped over a digital elevation model. [1]

However, the use of remote sensing alone as a single instrument to determine elephant impact and habitat alteration can also be misleading due to the influence of ruminants and other ungulates on vegetation structure [3]. Field assessments of certain variables are thus crucial. This is in particular valid for the interaction in African study sites with antelopes, since these often use openings created by elephants as foraging grounds, thereby in many cases destroying undergrowth vegetation without affecting tree canopy. This alteration of underground vegetation which affects the elephants’ foraging grounds cannot be detected by satellite imagery but might have a severe impact on habitat suitability due to decreased biomass availability. Furthermore, especially in vast areas or those not intensely covered by ranger patrols, population density as a critical aspect has to be determined in the field, through for instance the dung count method using line transects [4], direct observations or trails and footprints, allowing for upscaling of population per hectare. Also, spectral scans of preferred elephant forage vegetation types using multispectral radiometers improve the accuracy of large-scale mapping using Landsat imagery for NDVI.

Tagging elephants with satellite collars equipped with Global Positioning Systems (GPS) allows pinpointing of precise elephant locations more efficiently compared to the challenging direct observations in the wild, in particular in forested habitats with dense canopy cover and undergrowth vegetation. Advancements in technology in the last 20 years allows in these days to collect high quality spatial and temporal data due to more long-lasting collars which log location points at hourly intervals. Satellite tracking of individuals, especially if carefully selected, give valuable insights about range metrics and migration patterns as well as habitat use. Furthermore, the data collected from spatial datasets also provide information about elephant behaviour and identify patterns, that might potentially remain undetected by field observations and remote sensing alone [5]. An innovation in the utilisation of GPS tags can be found in the context of illegal poaching of elephants by deterring poachers from killing and transportation of animal parts due to tracking options by satellites. Furthermore, researchers now recognised the use of GPS tagged elephants for further research in conservation and ecology, due to their significance as sensible living sensors that provide information about changes in their environment.

Figure 2: Map of the elephant individual “Willow's” annual ranges in the Amboseli National Park Kenya, during the two-year tracking period. It shows that she made a pronounced shift to a new dispersal area in the second year. [5]

Figure 2: Map of the elephant individual “Willow’s” annual ranges in the Amboseli National Park Kenya, during the two-year tracking period. It shows that she made a pronounced shift to a new dispersal area in the second year. [5]

Summarizing all these collected data into GIS applications allows for the final modelling of habitat suitability maps, their analysis as well as visualisation of migratory patterns including corridors – a so-called ‘bottleneck’ allowing elephants to migrate between more substantial habitat expenses [6,7]. Furthermore, potential conflict zones regarding anthropogenic interaction can be determined and their visualisation used in communication with local stakeholders. The ranges of elephants within specific research areas have in a variety of studies been successfully determined, as also have been habitat fragmentation of study sites due to loss of corridors and the need for conservation management to address these issues [8]. With all this information gathered, remote sensing and GIS applications allowed researchers to understand the complex ecological requirements for elephant conservation and gave them insights into drivers that otherwise might have remained undetected. They now have the tools to accurately assess potential landscape-related issues affecting elephants and habitats, allowing them to co-work more efficiently with local authorities and wildlife management. All in order to develop conservation strategies that offer the best potential output to mitigate impacts to elephants, their natural environment and human neighbors.

Figure 3: Seasonal habitat use by Elephants (Loxodonta africana) in the Mole National Park of Ghana. [2]

Figure 3: Seasonal habitat use by Elephants (Loxodonta africana) in the Mole National Park of Ghana. [2]

References

[1] Bohrer, G., Beck, P. S., Ngene, S. M., Skidmore, A. K., & Douglas-Hamilton, I. (2014). Elephant movement closely tracks precipitation-driven vegetation dynamics in a Kenyan forest-savanna landscape. Movement Ecology, 2(1), 2.

[2] Ashiagbor, G., & Danquah, E. (2017). Seasonal habitat use by Elephants (Loxodonta africana) in the Mole National Park of Ghana. Ecology and evolution, 7(11), 3784-3795.

[3] Jordaan, M. (2012). The role of remote sensing for sustainable elephant management in South Africa. Four medium sized game reserves as case studies. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 259-264.

[4] Baskaran, N., Kannan, G., Anbarasan, U., Thapa, A., & Sukumar, R. (2013). A landscape-level assessment of Asian elephant habitat, its population and elephant–human conflict in the Anamalai hill ranges of southern Western Ghats, India. Mammalian Biology-Zeitschrift für Säugetierkunde, 78(6), 470-481.

[5] Sowers, M., Fishlock, V. & Manor, T. (2015). Mapping a future for Kenya’s Amboseli elephants. Available online: http://www.esri.com/esri-news/arcnews/summer15articles/mapping-a-future-for-kenyas-amboseli-elephants. Accessed: 05.08.2018

[6] Sukumar, R., Venkataraman, A., Cheeran, J. V., Mujumdar, P. P., Baskaran, N., Dharmarajan, G., … & Narendran, K. (2003). Study of elephants in Buxa Tiger Reserve and adjoining areas in northern West Bengal and preparation of conservation action plan. Final Report. Centre for Ecological Sciences, Indian Institute of Science, Bangalore.

[7] Venkataraman, A. (2005). What is an Asian elephant (Elephas maximus) corridor. Right of Passage: Elephant Corridors of India. Wildlife Trust of India, New Delhi, 24-33.

[8] Zhang, L., Dong, L., Lin, L., Feng, L., Yan, F., Wang, L., … & Luo, A. (2015). Asian elephants in China: estimating population size and evaluating habitat suitability. PloS one, 10(5), e0124834.

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