Remotely sensing fruit presence and nutritional content in complex landscapes

An animal’s survival depends on its ability to successfully navigate a dynamic resource landscape that varies in space and time. To study animal cognition in ecologically-relevant scales and settings, there is a need for reliable and efficient measures of nutritional resource distribution and quality. Hyperspectral imagery leverages the differential surface reflectance to estimate the relative chemical composition of a pixel, and may therefore enable remote sensing the distribution of nutrients at the landscape-scale. To explore the potential of this method in wild settings, we used airborne hyperspectral imagery with ground-based field spectroscopy and high-throughput wet chemistry data to predict nutrients present across an apple orchard landscape in Ravensburg, Germany. In this pilot study, we collected data on 24 apple trees over a four week period preceding harvest. We used spectral samples taken on the ground with a field spectrometer to create a spectral library of leaf and fruit samples. Simultaneously, we flew a hyperspectral drone (Headwall CoAlign) to collect hyperspectral voxels that were then spectrally unmixed to determine the endmember abundance in each pixel. After predicting the presence of fruit in a pixel, we then used the relationship between fruit reflectance and the sugar content to predict the amount of sugar available within a pixel. Our results indicate that apple sugar content is correlated with lower reflectance of the fruit in the near infrared. We are able to predict fruit presence on a pixel basis with 85% accuracy, and to predict sugar content using individual fruit reflectance with 80% percent accuracy. From this information, we can then extrapolate to create a prediction of nutritional elements on a landscape. Our approach demonstrates strong potential for use as a means of remotely sampling the nutritional landscapes in which wild animals live. This will open exciting opportunities for ecological studies at the landscape scale, including animal behavior researchers and movement ecologists to test detailed hypotheses related to animal movement and decision-making.