Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status

Abstract

Remote sensing is a key technology for precision agriculture to assess actual crop conditions. Commercial, high-spatial-resolution imagery from aircraft and satellites are expensive so the costs may outweigh the benefits of the information. Hobbyists have been acquiring aerial photography from radiocontrolled model aircraft; we evaluated these very-low-cost, very high-resolution digital photography for use in estimating nutrient status of corn and crop biomass of corn, alfalfa, and soybeans. Based on conclusions from previous work, we optimized an aerobatic model aircraft for acquiring pictures using a consumer-oriented digital camera. Colored tarpaulins were used to calibrate the images; there were large differences in digital number (DN) for the same reflectance because of differences in the exposure settings selected by the digital camera. To account for differences in exposure a Normalized Green-Red Difference Index [(NGRDI = (Green DN ) Red DN)/(Green DN + Red DN)] was used; this index was linearly related to the normalized difference of the green and red reflectances, respectively. For soybeans, alfalfa and corn, dry biomass from zero to 120 g m )2 was linearly correlated to NGRDI, but for biomass greater than 150 g m )2 in corn and soybean, NGRDI did not increase further. In a fertilization experiment with corn, NGRDI did not show differences in nitrogen status, even though areas of low nitrogen status were clearly visible on late-season digital photographs. Simulations from the SAIL (Scattering of Arbitrarily Inclined Leaves) canopy radiative transfer model verified that NGRDI would be sensitive to biomass before canopy closure and that variations in leaf chlorophyll concentration would not be detectable. There are many advantages of model aircraft platforms for precision agriculture; currently, the imagery is best visually interpreted. Automated analysis of within-field variability requires more work on sensors that can be used with model aircraft platforms.

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

“…The results shown so far are based on a VI that is calculated in a similar manner as the NDVI but using information from the blue and green spectral range. Visible light VIs, such as the NGRDI, are often used to characterize vegetation if NIR information is lacking (Pérez et al 2000; Meyer and Neto 2008; Raymond et al 2005). Due to their low costs and low weight, consumer-grade true colour (RGB) digital cameras are particularly suitable for assessing green vegetation using UAS-based imaging systems (Torres-Sánchez et al 2014; Saberioon et al 2014; Hoffmann et al 2016a; Goodbody et al 2017; Jannoura et al 2015).…”

“…Figure 6 shows scatter plots of T r versus the two tested VIs (NGRDI and NGBDI) as well as the calculated dry and wet edge for a bin size of 0.01 (solid line) and a bin size of 0.05 (dashed line) for three flights. While the NGBDI has a range comparable to the NDVI with most values between 0 and 0.8, the NGRDI has a significantly steeper dry edge with a smaller range of values (Raymond et al 2005). In general, negative values are considered to be associated with soil pixels while positive values represent vegetated pixels.…”

Estimating tree heights with images from an unmanned aerial vehicle

“…Forestry work with UAV platforms started in the 2000s with model planes and helicopters (Hongoh et al 2001;Hunt et al 2005;Fritz et al 2013). Recently they have been used with consumer-grade cameras and low-cost systems in order to make surveys more efficient.…”

Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform

“…Blimps have been used to map spatial variability between and within agricultural (rice and soybean) fields (Inoue, et al 2000) and to monitor gully erosion (Ries and Marzolff 2003). Remote-control helicopters were used to generate maps of crop status (Sugiura, et al 2005) and model aircraft were used as the platform to remotely sense crop biomass and nitrogen status (Hunt Jr., et al 2005). Additionally, a high altitude unmanned aerial vehicle was used to monitor crop ripeness and weeds in a coffee plantation (Herwitz, et al 2004).…”