Very high resolution Crop Surface Models (CSM) from UAV-based stereo images for rice growth monitoring in Northeast China

Juliane Bendig, Maximilian Willkomm, Nora Tilly, Martin Leon Gnyp, Simon Bennertz, Victoria I S Lenz-Wiedemann, Georg Bareth, Yuxin Miao, Qiang Cao

Research output: Contribution to journalArticlepeer-review

Abstract

Unmanned Aerial Vehicles (UAV) 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 very high resolution multi-temporal Crop Surface Models (CSM) for rice growth monitoring by means of low-cost equipment. The concept of the generation of multi-temporal CSM 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). UAV 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 CSM in different phenological stages. In this contribution, the method, that has already been used for barley (Bendig et al. 2013), is applied to a different crop in a different environment. The study area is an experiment field for rice in Northeast China (Sanjiang Plain). Two rice cultivars were planted and treated with different amounts of N-fertilizer. In July 2012 three UAV-campaigns were carried out. Additionally, further destructive and non-destructive field data were collected. The UAV-system is an MK-Okto by Hisystems (www.mikrokopter.de) equipped with a high resolution optical consumer camera. The self-built and self-maintained system has a payload of up to 1 kg and 15 minutes mean endurance and can be operated up to a wind speed of less than 19 km/h. Stereo images were captured at a flying height of 50 m and a 44% side and 90% forward overlap. The images are processed into CSM 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. 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.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
Journalgis.Science - Die Zeitschrift fur Geoinformatik
Issue number1
StatePublished - Jan 1 2015

Keywords

  • Agriculture
  • Biomass
  • DEM
  • Multi-temporal data
  • Plant height
  • Rice
  • UAV

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