Multi-sensor data acquisition for assessing the condition of vegetation

(1)

Multi-Sensor data acquisition for assessing the condition of vegetation

S. Stemmler*

^a,b,c

, D. Wiedenmann*

^a

a

Fraunhofer-Institute for Physical Measurement Techniques IPM, Freiburg, Germany;

^b

Chair of Remote Sensing and Landscape Information Systems FeLIS, Albert-Ludwigs-University Freiburg, Germany;

^c

Department of Sustainable Systems Engineering INATECH, Albert-Ludwigs-University

Freiburg, Germany

ABSTRACT

The pressure on vegetation, whether forests, meadows or cultivated areas, is becoming increasingly greater. Climate change, extreme weather and ever higher yields taking place at the same time are creating enormous challenges for areas under cultivation. Drought stress, heavy rains and cultivation of monocultures stress both, the soil and the crops themselves. Regular monitoring of the crops or trees as well as soil condition is essential for a sustainable land use. The use of unmanned aerial vehicles (UAVs) for aerial structural surveys, the recording of soil parameters such as soil temperature, soil moisture and gas exchange have so far mostly been carried out independently of each other. Combining these measurement techniques, a holistic picture of the state of these ecosystems becomes possible.

The Fraunhofer-Institute for Physical Measurement Techniques IPM presents a coherent process chain for the fully comprehensive recording of ecosystems. A recording by means of LiDAR systems from the ground, multispectral aerial images, terrestrial laser scans and the recording of nitrous oxide emission.

Thus, we obtain a full structural image of the ecosystem enriched with metadata on plant condition and soil parameters.

This forms the basis of an analysis of the overall condition of the full ecosystem. We present the results of the different sensors and the fused data of a first measurement campaign.

Keywords: Multi-sensor, UAV, laser scanning, photogrammetry, soil parameters, data fusion, nitrous oxide measurement

1. INTRODUCTION

Vegetation, whether forests or cultivated land, is the basis for life on Earth. Due to increasingly frequent extreme weather events, these ecosystems are under strong pressure. In forests in southern Germany, one can observe a strong dieback, especially of coniferous trees. In agriculture, mainly due to the cultivation of monocultures, fertilizers have to be applied heavily and meanwhile irrigation is also necessary.

Agriculture in Germany generates about 14% of greenhouse gases and is the largest emitter of nitrogen compounds.

Direct nitrous oxide (N2O) emissions from agricultural soils contribute significantly to greenhouse gas emissions. N2O has a global warming potential (GWP) of

~

280, substantially higher than the value of CH4 (GWP

~

32), which is next to CO2 the most important anthropogenic greenhouse gas [1]. The oxidation of ammonium to nitrate and the incomplete reduction of nitrate are responsible for the production of N2O in soils. High nitrate concentrations in soils are therefore often the cause for N2O production [2]. Since 1990, there has been no visible trend in Europe towards a reduction in estimated N2O emissions from agricultural soils [3]. The biggest share of N2O emissions (54%) comes from livestock farming; another significant share is due to agricultural land use. Nitrogen fertilization plays a major role in this.

Numerous studies prove the connection between nitrogen fertilization and N2O emission. About one third of this can be attributed to the use of fertilizers. Thus, a reduction of the amount and a more precise placement of fertilizers close to the plants could reduce the N2O emissions.

If we look at forests, emissions of nitrogen compounds are significantly lower. Here it is also important to distinguish that conifer-dominated forests emit larger amounts than deciduous forests [4].

We present a process chain for the fully comprehensive recording of vegetation areas. A combination of drone imagery with RGB sensors and thermal sensors, terrestrial laser scans (TLS) and nitrogen measurements.

One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

in: Neale, C.M.U.; Maltese, A.; Society of Photo-Optical Instrumentation Engineers – SPIE: Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII. Proc. SPIE 11856, Paper 118560L (2021); doi: 10.1117/12.2599758

(2)

2. MEASUREMENT SYSTEMS

To capture complex environments like forests, different measurement techniques are used. The following section describes the equipment used for this measurement campaign.

2.1 Nitrous oxide measurement system

The main setup components are the ICL (Nanoplus, Gerbrunn, Germany), a multi-reflection long-path cell (720 cm optical path) and a three-stage thermoelectrically cooled photodetector (Vigo System, S.A., Ożarów Mazowiecki, Poland). Schematically, the setup is depicted in Figure 1 (left). Peripherals include the TEC driving electronics (Meerstetter Engineering GmbH, Rubigen, Schweiz), an in-house development of a power-distribution-PCB with deep discharge protection, an accumulator of about 5000 mAh (Hacker Motor GmbH, Ergolding, Germany) and the gas flow system consisting of a pump (KNF Neuberger GmbH, Freiburg, Germany) on the gas outlet side and a DAQ-board (Measurement Computing, Bietigheim-Bissingen, Germany) to digitize the detector signal. The size and the weight of the whole setup are mainly determined by the optic stage. The gas flow system was designed to regulate the absolute pressure inside the long-path cell to roughly 290 mbar, as well as to restrict the gas flow to approximately 1 l/min. The stated pressure value is an empirical value coming from a trade-off between maximizing the absorption signal of N2O and the opposed minimizing of line-broadening effects as indicated by simulations using the HITRAN database [5]. All components were combined in one housing. To dissipate the waste heat of the electrical components, a cooling rod and tow fans were included in the housing. To collect the gas on the ground, a hood with tow gas inlets was used. The volume of the hood is continually pumped into the measurement cell and back to the hood. Through this the concentration of N2O increases by time and the important measured variable is the N2O flux over the time. In order to log peripheral parameters, such as soil humidity (SMT100), air humidity (SHT12), long-path cell pressure and gas temperature (MLP3112A5), the according sensors were implemented in the inlet of the long-path cell. A GPS tracker (Navilock, Berlin, Germany) was integrated to correlate the measured data with the exact field position and other field data.

Figure 1. Left: Schematic view of the setup. For overview reasons, not all reflections in the long-path cell (mirror-to-mirror- cell width 15 cm) are drawn. Right: Sample acquisition in real live with the whole setup.

N2O in air is detected via TDLAS with a 4.53µm DFB-ICL as light source. The laser emission wavelength range, was chosen according to the (largest) N2O photo absorption cross section, located roughly around 4.5 µm (2220 cm^-1) Figure 2 (left).

The operating temperature of the laser was set to 19°C; the custom defined laser current was chosen to start on a constant level Izero and then to change into a ramp structure from Ilow to Ihigh, Figure 2 (right). To obtain a stable data acquisition with reproducible triggering of the detector, IZero (10 mA) is intentionally set below the lasing threshold (35 mA). As for the regular data acquisition, the custom current curve was repeated with 1000 Hz.

(3)

Figure 2. Left: Absorbance of different N2O and H2O concentrations in air as a function of wavenumber. The wavenumber axis limits are chosen accordingly to the available spectral range of the emitted laser radiation. Right: Laser diode current as function of time with data acquisition.

The data analysis in this setup is purely digital, i.e., the detector signal is directly fed into a DAC and forwarded as a tuple of “time” and “voltage” to the laboratory PC. The smallest “data unit” is one current cycle, Figure 2 (right). After acquiring a set of approx. 60 cycles, a baseline correction with a third-degree polynomial fit is carried out. The outcome then passes through a Butterworth (6th order, 15 kHz cut-off frequency) low-pass filter. The intermediate results of the data processing steps are shown in Figure 2 (right). Eventually, the measured gas species concentration is correlated to the amplitude of the derivatives Dn. Therefore, the peak-peak value of each derivative is calculated, Figure 3. (left). At the beginning and at the end of each measurement a reference gas with 400 ppm N2O is used to calibrate the system, shown in Figure 3 (right). In order to track laser stability variations, i.e. to clearly distinguish whether the gas concentration is changing, or the radiation intensity fluctuates, a reference point is chosen in the detector signal at a wavelength where no absorption occurs.

Since 60 (data) periods are acquired, one data point of the measurement system is an average of these periods resulting in a 6-tuple containing a time stamp, the averaged reference value, and averaged amplitude of each of the four derivatives.

Figure 3. Left: Normalized derivatives D1 to D4. Right: Sample data from a field measurement at a corn field, with reference gas.

(4)

2.2 Multisource 3D-measurement

For structure acquisition as a three-dimensional model, satellite images or image data, taken with drones, are usually used. Using structure-from-motion algorithms, 3D point clouds are calculated from these images [6].

To successfully compute a point cloud from drone image data, the images must overlap. Optimally, an overlap along the flight direction of 80% and 50% in the transverse direction is used. To merge the data of several flights there are two options: a high precision GNSS or ground control points (GCP) are mandatory [7][8]. By means of this GCP, all created point clouds can be superimposed and fused together in post processing.

There are different procedures for the creation of terrestrial laser scans. By using targets, several point clouds taken from different points of view can be merged. Often the manufacturer's own software offers an automatic fusion of the point clouds based on the targets. Other systems rely on visual odometry and the point clouds are fused directly during the acquisition without any further steps. The difficulty with terrestrial laser scanning in the forest is the very limited GNSS reception. Therefore, for the fusion of point clouds from photogrammetry and TLS, GCP must be used.

3. MEASUREMENT CAMPAIGN

In this measurement campaign, a heavily damaged forest area in the south of Germany was recorded. The investigated area is about 1 ha in size and is characterized by a very heterogeneous stand. High ground vegetation, pines and a lot of deadwood are represented. Figure 4 is showing the examined area; the red flags marking the points of nitrous oxide measurements and the blue flags the scan positions of the TLS.

Figure 4. Aerial image of the examination area; red flags marking the points of nitrous oxide measurements and the blue flags the positions of the terrestrial laser scans

(5)

3.1 Photogrammetric measurement and terrestrial laser scanning

Several drone flights were carried out for the 3D reconstruction. The sensors and flight parameters used are shown in Table 3.

Table 3. Parameters of the four flights and the camera setups.

A point cloud was calculated from all 483 RGB images. Figure 5 shows the calculated point cloud, which consists of a total of 348 million points, corresponding to 34800 points per square meter. The individual trees are easily recognizable and due to the double grid flight pattern with a camera angle of 70°, the trunks of the trees are also relatively well captured.

Figure 5. Sideview of the photogrammetric point cloud with 348 Mio. Points.

In order to obtain a greater point density of the ground vegetation, a total of 20 TLS scans were carried out in the studyarea using a Leica RTC 360 scanner. Five of the scan positions are shown as blue flags in figure 4. Due to the cameras integrated in the scanner, the scan points also contain RGB information. Only in the crown areas of the trees, no color data is contained in the scans. The photogrammetric point cloud and the point cloud of the terrestrial scans were fused by using GCP. The comparison in Figure 6 shows the photogrammetric point cloud on the left, the TLS scan in the middle and the fused data set on the right.

Flight height above ground

Sensor Camera angel Flight pattern Ground sampling distance

50 m RGB 12.4 MP 80° Double grid 2,2 cm/Px

50 m Thermal 0.2 MP 70° Double grid 14 cm/Px

(6)

Figure 6. Left: Photogrammetric point cloud; Middle: Terrestrial lasers can; Right: Photogrammetric and lasers fused together.

The thermal camera images were interpolated using the high-resolution RGB images. Thus, even with the low resolution of the thermal camera, a point cloud with 22 million points was calculated. This corresponds to a point density of 2200 points per square meter. A section of the point cloud from the thermal images can be seen in Figure 7.

Figure 7. Snapshot of the photogrammetric point cloud with color coded with the output of the thermal camera.

3.2 Nitrous oxide measurement

The nitrous oxide measurements were carried out with the measurement system described in section 2.1. The forest floor was very overgrown, which made it difficult to find suitable places for a measurement. For the measurement, ambient air (330 ppm N2O) is normally measured first and then calibrated gas (400 ppm N2O). Due to a defective gas bag, only ambient air could be used as a reference for these measurements. After measuring the ambient air for a few minutes, the measuring hood was placed on the point of measurement. At the end of the measurement, ambient air was again fed into the system and measured. The rubber strips were attached to the hood to ensure a better seal of the chamber on uneven and overgrown ground (Figure 8).

(7)

Figure 8. Left: Sample acquisition at Measurement point 1 with the whole setup. Right: Detail of the measuring hood.

The nitrous oxide measurements were carried out at four measuring points, which were distributed over the study area.

This measuring points are shown in Figure 4 with red markers (MP 1-4). The data of these measurements were saved and evaluated after the measurement campaign. The results are shown in Figure 9. Small nitrous oxide flows can be seen at measuring points 2 and 3 (Figure 9, top right and bottom left). After a few minutes of ambient air, the Nitrous oxide flow starts. After about 8 minutes, the measuring hood was removed from the measuring point and the gas concentration in the measuring cell slowly decreased again. The decrease in the nitrous oxide concentration is significantly slower than in previous measurements. This may be due to the very low flow with which the volume of the cell is flushed. In addition, the differences in concentration between the ambient air and the sample gas are very small. In contrast, there is no discernible nitrous oxide flow at measuring points 1 and 4 (Figure 9 top left and bottom right). Due to the dense vegetation on the ground, a significantly lower nitrous oxide flow was to be expected than with bare and fertilized ground for example white in corn cultures. The differences in the four measuring points can come from the significantly different nature of the soil in the observed area. For example, there are places with exposed forest floor with leaves and moss, areas overgrown with grass and areas with dense bushes and small trees.

Figure 9. Measurement data from measurement point 1 (top, left), 2 (top, right), 3 (bottom, left) and 4 (bottom right) with smoothing by moving average of 50 points.

(8)

From the measured flow and the area of the measuring hood, a value of 24,51 ppm nitrous oxide per minute per square meter results for measuring point 2 and a value of 21,50 ppm nitrous oxide per minute per square meter for measuring point 3. No calculation was made for measuring points 1 and 4 due to the low or non-measurable flow.

4. OUTLOOK

The measurement campaign carried out forms the basis for further analyses of vegetation areas. The process chain can equally be adapted for areas, such as agricultural land. In 2022, such a measurement campaign will be carried out at regular intervals during the whole vegetation period. In this way, an understanding between plant growth, temperature influence and nitrous oxide emissions will be developed. Regular monitoring of forests and agricultural areas will then allow proactive action before damage occurs or early intervention to preserve these habitats. The information gained will be used in future to minimize ecological and economic impacts in anthropogenic farmland and natural ecosystems.

Nitrous oxide measurements can be used to detect overfertilization on agricultural land and the development of new fertilizers and fertilization methods. This information can be used to minimize the amount of fertilizer and thus emissions of climate-damaging gases. In this way, social value can be created, especially in the field of agriculture.

REFERENCES

[1] EPA, U. S. and OA: Understanding Global Warming Potentials US EPA, https://www.epa.gov/ghgemissions/understanding-global-warming-potentials (10 October 2018).

[2] Tilman, D., Fargione, J., Wolff, B., D'Antonio, C., Dobson, A., Howarth, R., Schindler, D., Schlesinger, W. H., Simberloff, D., and Swackhamer, D.,” Forecasting agriculturally driven global environmental change,” Science, 292, 281–284 (2001).

[3] Rees, R. M., Augustin, J., Alberti, G., Ball, B. C., Boeckx, P., Cantarel, A., Castaldi, S., Chirinda, N., Chojnicki, B., Giebels, M., Gordon, H., Grosz, B., Horvath, L., Juszczak, R., Klemedtsson, A. K., Klemedtsson, L., Medinets, S., Machon, A., Mapanda, F., Nyamangara, J., Olesen, J. E., Reay, D. S., Sanchez, L., Cobena, A. S., Smith, K. A., Sowerby, A., Sommer, M., Soussana, J. F., Stenberg, M., Topp, C. F. E., van Cleemput, O., Vallejo, A., Watson, C.

A., and Wuta, M.,“Nitrous oxide emissions from European agriculture - an analysis of variability and drivers of emissions from field experiments,” Biogeosciences, 10, 2671–2682, doi:10.5194/bg-10-2671-2013 (2013).

[4] Pilegaard, K., Skiba, U., Ambus, P., Beier, C., Brüggemann, N., Butterbach-Bahl, K., Dick, J., Dorsey, J., Duyzer, J., Gallaghert, M., Gasche, R., Hovrath, L., Kitzler, B., Leip, A., Pihlatie, M., Rosenkranz, P., Seufert, G., Vesala, T., Westrate, H., Zechmeister-Boltenstern, “Factors controlling regional differences in forest soil emission of nitrogen oxides (NO and N2O),” Biogeosciences, 3(4), 651-661 (2006).

[5] Rothman, L. S. and Gordon, I. E., “The HITRAN Molecular Database,” Aip Conf Proc, 1545, 223–231, doi:10.1063/1.4815858, 2013.

[6] Stemmler, S. “Development of a high-speed, high-resolution multispectral camera system for airborne applications,”

Proc. SPIE 11785, Multimodal Sensing an Artificial Intelligence: Technologies and Applications II, 1178512 (2021), doi: 10.1117/12.2591990.

[7] Stemmler, S., Reiterer, A., “Hochpräzises Laserscanning aus der Luft - leichtgewichtige Sensortechnologie eröffnet neue Anwendungsgebiete durch die Fusion von 3D-LiDAR-Daten und 2D-Bilddaten,“ Photogrammetrie - Laserscanning - Optische 3D-Messtechnik: Beiträge der Oldenburger 3D-Tage 2019, Wichmann, Berlin, 214-221 (2019).

[8] Reiterer, A., Frey, S., Koch, B., Stemmler, S., Weinacker, H., Hoffmann, A., Weiler, M., Hergarten, S., „Laser- and multi-spectral monitoring of natural objects from UAV,“ EGU General Assembly Conference Abstracts, 1462 (2016).