Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W2, 45-50, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W2-45-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
16 Aug 2013
Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring In Northeast China
J. Bendig1, M. Willkomm1, N. Tilly1, M. L. Gnyp1, S. Bennertz1, C. Qiang2,3, Y. Miao2,3, V. I. S. Lenz-Wiedemann3,1, and G. Bareth3,1 1Institute of Geography (GIS & Remote Sensing Group), University of Cologne, 50923 Cologne, Germany
2College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100094, China
3ICASD – International Center for Agro-Informatics and Sustainable Development (http://www.icasd.org)
Keywords: Agriculture, Biomass, DEM, Multi-temporal data, Plant height, Rice, UAV Abstract. Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed geodata in the last years (Hardin & Jensen 2011). Various applications in numerous fields of research like archaeology (Hendrickx et al., 2011), forestry or geomorphology evolved (Martinsanz, 2012). This contribution deals with the generation of multi-temporal crop surface models (CSMs) with very high resolution by means of low-cost equipment. The concept of the generation of multi-temporal CSMs using Terrestrial Laserscanning (TLS) has already been introduced by Hoffmeister et al. (2010). For this study, data acquisition was performed with a low-cost and low-weight Mini-UAV (< 5 kg). UAVs in general and especially smaller ones, like the system presented here, close a gap in small scale remote sensing (Berni et al., 2009; Watts et al., 2012). In precision agriculture frequent remote sensing on such scales during the vegetation period provides important spatial information on the crop status. Crop growth variability can be detected by comparison of the CSMs in different phenological stages. Here, the focus is on the detection of this variability and its dependency on cultivar and plant treatment. The method has been tested for data acquired on a barley experiment field in Germany. In this contribution, it is applied to a different crop in a different environment. The study area is an experiment field for rice in Northeast China (Sanjiang Plain). Three replications of the cultivars Kongyu131 and Longjing21 were planted in plots that were treated with different amounts of N-fertilizer. In July 2012 three UAV-campaigns were carried out. Establishment of ground control points (GCPs) allowed for ground truth. Additionally, further destructive and non-destructive field data were collected. The UAV-system is an MK-Okto by Hisystems (http://www.mikrokopter.de) which was equipped with the high resolution Panasonic Lumix GF3 12 megapixel consumer camera. The self-built and self-maintained system has a payload of up to 1 kg and an average flight time of 15 minutes. The maximum speed is around 30 km/h and the system can be operated up to a wind speed of less than 19 km/h (Beaufort scale number 3 for wind speed). Using a suitable flight plan stereo images can be captured. For this study, a flying height of 50 m and a 44% side and 90% forward overlap was chosen. The images are processed into CSMs under the use of the Structure from Motion (SfM)-based software Agisoft Photoscan 0.9.0. The resulting models have a resolution of 0.02 m and an average number of about 12 million points. Further data processing in Esri ArcGIS allows for quantitative comparison of the plant heights. The multi-temporal datasets are analysed on a plot size basis. The results can be compared to and combined with the additional field data. Detecting plant height with non-invasive measurement techniques enables analysis of its correlation to biomass and other crop parameters (Hansen & Schjoerring, 2003; Thenkabail et al., 2000) measured in the field. The method presented here can therefore be a valuable addition for the recognition of such correlations.
Conference paper (PDF, 849 KB)


Citation: Bendig, J., Willkomm, M., Tilly, N., Gnyp, M. L., Bennertz, S., Qiang, C., Miao, Y., Lenz-Wiedemann, V. I. S., and Bareth, G.: Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring In Northeast China, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W2, 45-50, https://doi.org/10.5194/isprsarchives-XL-1-W2-45-2013, 2013.

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