The Abundance of Dunlin in Northern Scotland Can Be Predicted $\text { Abundance }=-372.1 \log (\text { NIR reflectance })-18.6(D)+748.1$

Multiple Choice

The abundance of dunlin in northern Scotland can be predicted using the following multiple regression model $\text { Abundance }=-372.1 \log (\text { NIR reflectance }) -18.6(D) +748.1$ where D is the distance from pool-system edges, and NIR (near infrared) reflectance represents the scaled intensity of near infrared radiation reflected from the ground which is sensitive to ground wetness) . Suppose you were provided with the NIR band of Landsat imagery and a vector data layer of pool systems covering the area, which GIS operation or sequence of GIS operations should you use to create an abundance map?

A) Reclassify the NIR band image into a multi-class map layer, rasterise the pool systems layer and reclassify it into a multi-class map layer, and combine the reclassified multi-class maps via map algebra with the model.
B) Reclassify the NIR band image into a multi-class map layer, rasterise the pool systems layer and generate a proximity map using it as the input layer, reclassify the proximity map into a multi-class map layer, and combine the reclassified multi-class maps via map algebra with the model.
C) Reclassify the NIR band image into a multi-class map layer, generate a proximity map using the pool systems as the input layer, reclassify the proximity map into a multi-class map layer, and combine the reclassified multi-class maps via map algebra with the model.
D) Generate a proximity map using the pool systems layer as the input layer, and combine the proximity map and the NIR band image via map algebra with the model.