“If the bee disappeared off the surface of the globe, then man would have only four years of life left”, that is what Albert Einstein predicted about a hundred years ago. Only thirty years ago, cars were covered with a film of dead insects after long journeys, and you have seen car-loving enthusiasts washing and polishing their cars to avoid defects in the paintwork. Nowadays, you can drive for hundreds of kilometers without worrying about your car being an insect-graveyard. Insects account for about 70% of global species, and recent estimates predicted the number of extant arthropod species to be about 81% . This also includes a global decline in pollinators such as bumblebees, butterflies and, wild as well as domesticated, bees. Bees are fundamental ecologically and economically pollinators for wild vegetation as well as cultivated crops, of which about 84% in Europe depend upon insect pollination [2, 3]. Recently, the economic value of ecosystem services provided by bees has been estimated at over $162 billion . The decline of global bee populations raised public awareness by a pollination threat of the multi-billion dollar worth Californian almond-crop in 2006  and by a recent study of German-Dutch scientists that reported an annual mean decline of 78% of the bee population over a period of 27 years . Many factors have been identified as the cause of this significant decline: anthropogenic stressors such as the increasing use of pesticides, land-use changes and environmental degradation in general, as well as the impact on the cognitive capacities of bees resulting in a disruption of foraging performance .
Using GIS and Remote Sensing to Map Bee Friendly Landscapes
More recent studies have showed how GIS and remote-sensing applications could help to assess and map landscape as well as beekeeping suitability to allow precision beekeeping and hence the promotion of colonies. Furthermore, modelling tools have been developed to map bee migration patterns in order to assess and evaluate changes in the timing of this plant-pollinator interaction. The ever-increasing capacity of remote sensing applications offers ecologists ample and novel opportunities to assess landscape suitabilities or pollination services by bees as a threatened ecosystem service.
Fuzzy Logic vs Multiple Criteria Decision Making for land-cover suitability analyses
Landscape characterization is the primarily used application to assess bee foraging ranges and migratory pollination variables. Aerial photography and satellite imagery, processed using suitability analyses in GIS, have been utilised in a variety of studies to link land cover properties as well as climatic changes with bee responses to habitat change and loss . Researchers from the US developed a spatially explicit model in order to identify potential sites for large apiaries based on local-scale landcover requirements for honey bees, using satellite data by the North Dakota Gap Analysis Program (NDGAP) processed using the Analytic Hierarchy Process – Multiple Criteria Decision Making (AHP-MCDM). They produced annual time series for North Dakota, a significant honey producing area, as well as predicted land-use changes. As a result, they highlighted the inability of existing land-cover products to monitor specific changes in landcover suitability for honey bees due to a lack of sufficient local accuracy . Zoccali et al. (2017) addressed this issue and highlighted, that the mainly used MCDM-AHP approach led to a variety of uncertainty in the results of many studies relating to beekeeping land suitability . Instead, they evaluated the fuzzy approach to avoid these imprecisions and uncertainties (often) caused by personal preferences in order to “fill the gap between Boolean logic and weighted linear combination”, providing a strong logic within the data. Supported by real conditions within apiculture, their results not only show a high accuracy but also that the production of reality-supported maps with a high spatial and temporal resolution does not necessarily need to be costly . The assessment of current, for a bee’s perspective, favourable landscape conditions are a fundamental part of predicting the suitability as a bee habitat and can not only be used to evaluate current populations but also to give beekeepers the incentives to use specific areas as foraging grounds.
Can we track bee density and spatial variation by the detection of individuals?
Individual detection of plant species is relatively easy and has been performed successfully for decades. Furthermore, ecologists have been successfully tracking larger animals utilizing remote sensing tools, even though confronted with difficulties due to the animal’s agility. However, what about small insects such as bees? Sara Galbraith and colleagues highlighted in a paper in 2015 the potentials of harmonic radar as well as LiDAR for tagging and tracking honeybees including their density and spatial variation . Even though not applicable for large-scale studies of migration patterns, the timed application of these techniques in selected areas can support habitat suitability models derived by for instance MODIS data, to predict bee distribution as well as migration patterns [9,10].
High spatial and temporal data are in particular useful to assess ecological processes within landscape settings and have been successfully linked to pollinator diversity  using NDVI data. Especially greening and browning, as well as PAR and leaf area index were critical indicators for mapping variables associated with bee foraging habitats . However, this is a relatively young field of application and advancements will hopefully be made within the near future to improve remotely sensed datasets to quantify and qualify geographical pollinator studies.
The future of assessing and mapping landscape suitability for bee habitats
With earth observation and GIS applications being a relatively young field of research developing at a high pace, the opportunities for novel approaches to assessing bee responses to habitats are expanding. Especially stress-responses to variables from land-use changes under anthropogenic influences in understudied and regional contexts should be a future objective. In this sense, interdisciplinary collaborations between ecologists and professionals within the field of remote sensing might fill the gap of linking bee functional traits to migration patterns and pollination services, leading to better mapping results and subsequently to enhanced planning for beekeepers and the promotion of precision beekeeping. Active remote sensing tools such as terrestrial and airborne LiDAR have shown promising applications to predict bee foraging and migration activities by evaluation of physiological vegetation parameters such as chlorophyll, hydrology, photoprotection or nitrogen content . In a nutshell, the opportunities of the utilization of remote sensing for geographical honeybee assessments have the ability to understand and promote pollinator habitats, therewith supporting both sides of the coin: avoiding a further decline of pollinator and honeybee abundances potentially leading to a controlled increase of colonies at the same time as ensuring pollination as an ecosystem service that is more than fundamental for a thriving ecosystem. By understanding bee migratory patterns and contributing to a loss of habitats, we can potentially avoid Einstein’s prediction becoming true and hence successfully conserve our shared environment.
References Stork, N. E., McBroom, J., Gely, C., & Hamilton, A. J. (2015). New approaches narrow global species estimates for beetles, insects, and terrestrial arthropods. Proceedings of the National Academy of Sciences, 112(24), 7519-7523.  Neumann, P., & Carreck, N. L. (2010). Honey bee colony losses.  Klein, S., Cabirol, A., Devaud, J. M., Barron, A. B., & Lihoreau, M. (2017). Why bees are so vulnerable to environmental stressors. Trends in ecology & evolution, 32(4), 268-278.
 Gallai, N., Salles, J. M., Settele, J., & Vaissière, B. E. (2009). Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecological economics, 68(3), 810-821.  Hallmann, C. A., Sorg, M., Jongejans, E., Siepel, H., Hofland, N., Schwan, H., … & Goulson, D. (2017). More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PloS one, 12(10), e0185809.  Galbraith, S. M., Vierling, L. A., & Bosque-Pérez, N. A. (2015). Remote sensing and ecosystem services: current status and future opportunities for the study of bees and pollination-related services. Current Forestry Reports, 1(4), 261-274.  Gallant, A. L., Euliss Jr, N. H., & Browning, Z. (2014). Mapping large-area landscape suitability for honey bees to assess the influence of land-use change on sustainability of national pollination services. PLoS One, 9(6), e99268.  Zoccali, P., Malacrinò, A., Campolo, O., Laudani, F., Algeri, G. M., Giunti, G., … & Palmeri, V. (2017). A novel GIS-based approach to assess beekeeping suitability of Mediterranean lands. Saudi journal of biological sciences, 24(5), 1045-1050.  Jarnevich, C. S., Esaias, W. E., Ma, P. L., Morisette, J. T., Nickeson, J. E., Stohlgren, T. J., … & Tan, B. (2014). Regional distribution models with lack of proximate predictors: Africanized honeybees expanding north. Diversity and Distributions, 20(2), 193-201.  Osborne, J. L., Clark, S. J., Morris, R. J., Williams, I. H., Riley, J. R., Smith, A. D., … & Edwards, A. S. (1999). A landscape‐scale study of bumble bee foraging range and constancy, using harmonic radar. Journal of Applied Ecology, 36(4), 519-533.  Levanoni, O., Levin, N., Pe’er, G., Turbé, A., & Kark, S. (2011). Can we predict butterfly diversity along an elevation gradient from space?. Ecography, 34(3), 372-383.  Nightingale, J. M., Esaias, W. E., Wolfe, R. E., Nickeson, J. E., & Ma, P. L. (2008, July). Assessing Honey Bee Equilibrium Range and Forage Supply using Satelite-Derived Phenology. In Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International(Vol. 3, pp. III-763). IEEE.