Combining Metric Aerial Photography and Near‐Infrared Videography to Define Within‐Field Soil Sampling Frameworks

Abstract

This paper investigates the combination of metric aerial photography and near‐infrared (NIR) videography data to improve the design of field‐survey sampling frameworks. Spatial data collection can contribute up to 80% of the cost of deploying a Geographic Information System (GIS) based Decision Support System (DSS). The use of remotely sensed information, field survey using differential Global Positioning System (dGPS) and geostatistical interpolation methods maximises data quality for a given rate of sampling.

Medium‐format colour aerial photography and NIR videography were orthorectified to the national map base and mosaiced using ERDAS Imagine. The green and red layers of the aerial photography were combined with the NIR videography to form a false‐colour composite image. Two sampling strategies were tested. The first stratified sampling on a per field basis, creating four points per hectare, randomly located within each field. The second strategy used the remotely sensed information to identify within‐field variability classes for each field, using red‐green difference or normalised difference vegetation index (NDVI) models. These variability classes were used as a sub‐stratification framework with each class sampled at the same rate of 4 per hectare. For both strategies the sample points were generated within ESRI ArcView and were located in the field using dGPS. Maps of stone content were created using geostatistical methods and validated against samples collected on a 100 metre grid. It was concluded that combining the two image sources to create a within‐field stratification framework improved the precision of the results obtained from field‐survey.