International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
EVALUATION OF TERRESTRIAL LASER SCANNING FOR RICE GROWTH
MONITORING
N. Tilly ***, D. Hoffmeister ^4 H. Liang, Q. Cao", Y. Liu*, V. Lenz-Wiedemann 2d y Miao *¢, G. Bareth *¢
2 Institute of Geography (GIS & Remote Sensing Group), University of Cologne, 50923 Cologne, Germany -
(nora.tilly, dirk.hoffmeister, victoria.lenz, g.bareth)@uni-koeln.de
® Department of Plant Nutrition, China Agricultural University, 100193 Beijing, China, -
(giangcao, ymiao)@cau.edu.cn
* Five Star Electronic Technologies Co. Ltd., Fengtai District, Fangchengyuanyiqu, Building17, Riyuetiandi B,100078
Beijing - liuyj0001@yahoo.com, china.lh@gmail.com
‘International Center for Agro-Informatics and Sustainable Development (ICASD), www.icasd.org
KEY WORDS: TLS, Multitemporal, Agriculture, Crop, Change Detection, Monitoring
ABSTRACT:
The rapidly growing world population and the resulting pressure on the efficiency of agriculture require a sustainable development
of intensive field management with regard to natural resources. In this context, the use of non-destructive remote sensing
technologies to monitor status and change detection of plant growth is in the focus of research and application. In this contribution,
we evaluate the applicability of multitemporal terrestrial laser scanning (TLS) for rice growth monitoring. The test sites are located
around Jiansanjiang in Heilongjiang Province in the far northeast of China. The focus of the field experiment was on different
nitrogen fertilizer inputs during the growing period in 2011. To realize the monitoring approach, three campaigns were carried out
during the vegetative stage of rice plants. For all campaigns the terrestrial laser scanner Riegl VZ-1000 was used. The achieved
knowledge can be described in two parts. First, for each date the variability of plant height and biomass is detectable for the whole
experiment field and - more important - between the plots. Furthermore, differences in height and biomass related to edge effects can
be investigated for every single plot. The spatial distribution is visualized by Crop Surface Models (CSM), which are digital surface
models with a high resolution and accuracy achieved by the interpolation of the 3D point clouds. Secondly, the multitemporal
surveying approach enables the monitoring of the growth rate of the rice plants. Additionally, it is possible to detect and analyze as
well the spatial distribution of the changes by comparing the CSMs. Our results show that TLS is a suitable and promising method
for rice growth monitoring. Furthermore, the contemporaneous surveying with other sensors enables us to validate our measurements
and bares opportunities for further enhancements.
1. INTRODUCTION
The principle of Light Detection and Ranging (LIDAR) systems
is the computation of distances between a sensor and a target
with a laser beam (Jensen, 2007). This is possible by (i)
measuring the time between transmitting and receiving a pulsed
signal, (ii) calculating the phase shift in a sinusoidal continuous
beam, or (iii) detecting a laser dot on a target with a camera and
calculate the distance from the triangle between transmitter,
camera, and target (Shan & Toth, 2009; Kraus, 2004). Thus,
highly accurate 3D point clouds are obtained from the achieved
information. Depending on the used platform, it can be
distinguished between Airborne Laser Scanning (ALS),
Terrestrial Laser Scanning (TLS), and Mobile Laser Scanning
(MLS) (Vosselmann & Maas, 2010). The methods differ in
their accuracy, spatial resolution, covered area, and measuring
rate.
Generally, the investigation and monitoring of the earth's
surface by remote sensing techniques is object of research in
various fields of interests. However, in the field of agriculture,
laser scanning is only used for a few applications.
McKinion etal. (2010) established yield stability maps for
cotton and corn fields over a period of three years with ALS
measurements. At a smaller scale, Eitel et al. (2011) analysed
the nitrogen status of spring wheat using a TLS system with a
* Corresponding author: nora.tilly@uni-koeln.de
green scanning laser. Based on a previous study (Eitel et al.,
2010), they detected the ability to quantify the crop nitrogen
status by the relationship between leaf chlorophyll and reflected
green laser light. In the context of precision farming,
Saeys et al. (2009) mounted a laser scanner on a combine
harvester to estimate the crop density while driving. The driving
speed can be adjusted automatically in order to maximize the
capacity of the harvester. The usability of multi-temporal TLS
to detect 3D crop changes, is presented by Hoffmeister et al.
(2010). In this study, patterns in the distribution of height
differences within a sugar-beet field were detected with a time-
of-flight laser scanner and visualized in Crop Surface Models
(CSM) and Crop Volume Models (CVM). Ehlert et al. (2008,
2009) established a measuring system with a triangulation and a
time-of-flight scanner. The estimated mean crop heights
(oilseed rape, winter rye, winter wheat, and grassland) were
correlated to fresh and dry biomass with good results (R?-0.75
to 0.99). The impact of different nitrogen fertilizer rates on
various cereals (barley, oat, and wheat) was investigated with a
phase-shift scanner by Lumme et al. (2008). They found a good
correlation between plant height and grain yield (R°=0.88 to
0.99).
The structure of rice plants is similar to the investigated cereals,
which suggests that laser scanning can also be used to
determine rice crop properties, Moreover, the importance of
