1. Introduction
Flower production is essential for the survival of most plant species in the world, because it relates directly to the number of seeds produced. As a side effect, flowers provide ecosystem services, such as food for many insects via the flower-pollinator relationship. Moreover, humans have benefited for thousands of years; for example, by cultivating honeybees (Apis mellifera) for honey production or for the pollination of fruit trees. Furthermore, not only are tree flowers, but also the subsequent fruits and seeds, energy banks for many animals. However, detailed information about tree flowers, particularly those of black locust (Robinia pseudoacacia L.), is very limited.
Black locust is one of the most criticized non-native tree species in Germany and Europe [
1] because its rootstocks spread into neighboring areas. Furthermore, black locust represses rare, native, and endangered species [
2]. However, black locust also offers many advantages and thus may represent an opportunity for European forests in these times of climate change. For example, it is a fast growing, sun light loving, and unpretentious tree and can grow in poor-nutrient areas, such as former brown-coal mining areas. In addition, black locust starts flowering early, often at an age of six years [
3] or younger. Many insects, especially
Hymenoptera, such as
A. mellifera, benefit from this service. Black locust honey is often sold as acacia honey [
4], and in Hungary, the production of this type of honey is very common and economically important [
3].
Many ecosystem structures and changes may be monitored via satellite data [
5,
6,
7,
8,
9,
10]. In forest ecosystems, the focus has been on the canopy structure [
11,
12,
13], especially the leaf area index (LAI) [
13,
14,
15,
16,
17], chlorophyll analysis [
13,
18,
19], and discoloration caused by diseases or disturbance [
20,
21,
22], particularly insects [
23,
24,
25,
26,
27,
28,
29,
30,
31]. In Africa, flower cycles have been monitored by airplane remote sensing [
32,
33] as a guide for beekeepers. However, unmanned aerial vehicles (UAVs) allow monitoring with a much higher spatial resolution of small, specific, and detailed vegetation structures, such as flowers. In addition, UAVs combine quick turnaround times in combination with lower operational costs. Therefore, UAV data may provide an important supplement to data from satellites, airplanes, towers [
34], and to field-collected data. In agriculture, the work done with information and communication technology is described as the fourth revolution. Smart farming is the key to developing sustainable agriculture, and UAVs are an important part of this strategy [
35]. Scientific and practical examples of UAV use include the monitoring of agave crops [
36], canola [
37,
38], maize [
39,
40], potato [
41,
42], wheat [
42,
43,
44,
45,
46,
47], sugarcane [
48], viticulture [
49,
50], peach trees [
51], olive trees [
52,
53,
54], and papaya trees [
55] in addition to the distribution of biological insecticides [
56] and the control of drainage systems in agricultural areas [
57]. UAVs are also a part of a new revolution in forestry management [
58]. For example, UAVs are now used to find bark beetles as early as possible [
59,
60], to monitor the infestation levels of pests [
61], to manage wild animals, develop forest inventories [
58,
62], monitor wind damage [
63], schedule projects, exploit forests [
64], and to quantify the expected seed production of beech trees [
65]. In plant ecology, UAVs have been used for LAI analysis [
66] and to analyze the structure of ultrafine dryland vegetation [
67]. Furthermore, Mc Neil et al. [
68] combined a UAV and ImageJ (a freeware Java-based image-processing program) to measure leaf-angle distribution in the canopies of broadleaved trees. MacInnis and Forrest [
69] used ImageJ to quantify the pollen deposition of two
Narcissus ssp. and strawberry (
Fragaria x ananassa). Flower-health classification supported by digital-imaging techniques with ImageJ is the topic of Lino et al. [
70].
In the present study, we use a UAV and flower analysis by ImageJ as an innovative and economical option to analyze the flowers of R. pseudoacacia and estimate the food base for Hymenoptera, especially A. mellifera. We investigate 13 different flying altitudes (Vertical Analysis), monitor a 50 × 50 m2 area (Horizontal Analysis), and seven sample trees (Flower Trees) in bloom in an eight-year-old black locust plantation. The flowers were counted, photographed, and weighed. Furthermore, we create two models: Model (1) solely base on the field-collected data, and Model (2), which combines the field-collected data with photographic data from the UAV.
The main idea of this study is to monitor the insect-biodiversity potential of black locust trees by indirect analysis. To demonstrate this method, we apply it to honeybees, although numerous opportunities exist for analyzing further insect species. Thus, this study represents a first step toward developing a method to analyze tree flowers, which may eventually evolve into an indirect method to analyze biodiversity. The research objectives of this study are (1) to adapt methods to classify flowers in black locust plantations via UAV red–green–blue (RGB) aerial imagery; and (2) to evaluate the number of flowers and the nectar production, and to assess the habitat for honeybees. The corresponding research questions are as follows:
Can UAVs be used to analyze flowers in R. pseudoacacia plantations?
- (1)
What is the best flying altitude (FA) for the UAVs?
- (2)
How much flower surface (2D) and volume (3D) can be detected by a UAV?
- (3)
How many flowers are present per hectare, and per tree?
Is this method suitable to monitor the structure of the temporary habitat for honeybees?
- (1)
What mass of nectar and honey (kg) can be produced by one hectare of eight-year-old R. pseudoacacia?
- (2)
What population of honeybees can survive for one year from the flower production of a one-hectare black locust plantation?
4. Discussion
The use of UAVs in agriculture [
36,
37,
38,
56,
57,
62], horticulture [
51], plant ecology [
66,
68,
89], and forestry [
59,
61,
62,
65] is increasing worldwide. However, UAVs were not heretofore used to quantify the flower production of black locust. In this work, by using a hexacopter and image analysis, we test a method to analyze the flowers of black locust trees in a short-rotation coppice on a previous brown-coal mining area in northeastern Germany. In addition, the flowers of seven sample trees were analyzed manually to calculate the number of flowers and to estimate the nectar and the honey production available as the food base for honeybees.
Based on the UAV and field-collected data, our model estimates 5.3 million flowers at the eight-year-old one-hectare black locust for the year 2017.
R. pseudoacacia produces a minimum of 1896 flowers, a mean of 5264 flowers, and a maximum of 15,559 flowers (analysis of Flower Trees). Williams et al. [
90] analyzed
Eucalyptus nitens flowers in Tasmania and found a minimum of 8 flowers per tree and a maximum of 211 flowers per tree [
90]. The flower production of
R. pseudoacacia is high compared with that of
E. nitens, but the flowers of
R. pseudoacacia are smaller than those of
E. nitens and the flower-insect relationships differ between the two tree species. Furthermore,
E. nitens flowers for three months (from January to March) [
91], whereas
R. pseudoacacia flowers only 5.5 days [
2]. Williams et al. [
90] counted flowers manually. An additional analysis done with the aid of UAVs would have been possible because the white color of
E. nitens flowers differs significantly from the colors of other elements, such as leaves, and branches, and rocks.
In the present study, 11% of the total 2D surface and 5.5% of the total volume (3D) of the
R. pseudoacacia flower area are captured by the UAV. For future studies, we recommend analyzing more than seven flower trees to obtain a more accurate coefficient of determination (regression) and more significant correlations between flower number and detected flower area. Horton et al. [
51] obtained an average detection success rate of 84.3% for peach flowers. However, peach plantations have the advantage that the trees flower before foliation. In addition, the stand density is lower, and the peach trees are smaller than black locust trees. Horton et al. [
51] explains that some of the white and light-pink flowers are similar in color to the branches and some parts of the ground. In Germany,
R. pseudoacacia normally flower at the end of May after foliation. Therefore, many flowers are concealed by leaves, branches, or other flowers.
Near-infrared cameras were useful for collecting debilitation of plants caused by nutrient deficiency (potassium) or insect attacks (green peach aphid or bark beetle) and for calculating the normalized density vegetation index [
37,
59,
61,
92]. In the present study, the blue channel is used to analyze the flowers in the UAV pictures. This corresponds to the description of Horton et al. [
51]: objects with high chlorophyll (weeds) reflect strongly in the near-infrared and green bands but poorly in the blue band. In our study, because of the chlorophyll, the leaves reflect strongly in the near-infrared and green bands. In the blue channel, the biomass (chlorophyll) reflects poorly, but the white flowers reflect strongly. Therefore, a noticeable boundary exists between flower and tree (and weed) biomass. Richardson et al. [
34] used the relative brightness of the red channel (red%) to analyze flowers of red maple trees. Because the flowers of red maple trees are red, the best boundary is obtained by using the red channel. Overall, flower analysis is possible in most cases with a RGB camera. The use of the blue channel to analyze the flowers of black locust works well. We tested various thresholds in the blue channel (110–160). Shaded and illuminated flowers (and some parts of the soil) are detected at threshold 110, whereas only illuminated flowers are detected at threshold 160. Consequently, at threshold 160 the User Accuracy Flower and the Producer Accuracy Biomass were 100%. However, 46.6% from the flowers are detected as biomass and get lost for the calculations (
Appendix A,
Table A1). When comparing all thresholds, threshold 140 performed best with respect to Overall Accuracy and Kappa Coefficient [
77,
78] and was therefore used in Horizontal and Flower Tree analyses to measure the flower area.
The Vertical Analyses are similar to that applied by Severtson et al. [
37] with the vertical FA ranging from 20 to 110 m above the starting point. The flower surface of the entire area (leaves, branches, stones, soil, etc.) ranges from 2.97% to 0.03% at FAs of 2.6 and 92.6 m above the crowns, respectively. This indicates that flowers occupy just a small fraction of the environment in black locust plantations, and that most flowers are detectable if the UAV flies as near as possible to the crowns. As of 12.6 m FA above crowns, the detected flower area decreases rapidly. The size of the flowers does not change, but the area monitored by the UAV increases. The GSD changes rapidly with increasing FA. Therefore, the white color of the flowers merges with the darker colors of the leaves, branches, weed, and soil. Consequently, the threshold per pixel decreases. The detected area remains essentially constant for all FAs greater than 50 m above the starting point. Radiometric calibration is not applied, because radiometric distortions, normally caused by atmospheric influences, were insignificant considering the very low FAs [
61,
93]. Furthermore, the detected area does not significantly increase if the flower number increases from 0 to 6000 flowers per tree: the number of flowers detected remains nearly constant, except for tree number 2 of 60% detected flower area. Tree number 2 had a large number of flowers (15,000 flowers per tree) compared with the other Flower Trees. Tree 2 was not in a different phenological state as the other trees. One explanation for the recognized large number of flowers and area is; tree 2 was one of the biggest (diameter, height, circular crown area) trees in the stand. Higher trees tree might benefit more from sun light for energy production, and the bigger circular crown area indicates a high number of branches that can produce more flowers altogether. The use of the crown-correction factor is advised if the ground-slope angle is greater than 10°, but it is also important if the tree height varies along and between the lines in the study area. Therefore, the crown-correction factor is one possible way to improve the precision of the results. Tree growth rate varies from tree to tree, which leads to variations in tree height, branches, and crown structure.
Model (2) of this study estimates 5.3 million flowers for one hectare of eight-year-old
R. pseudoacacia plantation. As a result, one bee hive could survive one year from the estimated honey base. In the previous brown-coal mining area of Lauchhammer, 40 hectares were planted with
R. pseudoacacia over the last 10 years. Moreover, older black locust forests and black locust in mixed stands are neighboring to the study area. Therefore, at least 40
A. mellifera hives could survive, if all replanted areas (40 hectare) have nearly the same number of flowers as the study area. In these times of loss of biodiversity [
94,
95,
96,
97], this study shows that re-planting of human dispelled landscape, such as former mining areas, can be beneficial to the food base, especially for honeybees. To understand the relationships between flower production and stand density, tree age, tree diameter, and tree height, further studies in different areas are recommended.
The estimated honey base of this study is 70 kg per hectare, which is the honey mass required per year for an average
A. mellifera hive [
85]. However, 70 kg per hectare is scant, compared with results from Hungary. In Hungary, nectar and honey yield of black locust is increases from year 6 to year 15. The maximum nectar yield is 836 kg/ha of nectar and 418 kg/ha of honey. The lowest yield is documented for year 36 with 384 kg/ha of nectar and 192 kg/ha of honey [
3]. These results of Hungarian scientists are consistent with those of Droege [
84], who reports that honey production of
R. pseudoacacia ranges from 195 to 1000 kg/ha. Therefore, the 70 kg/ha calculated in this study is lower than the lowest limit of Droege [
84] (195 kg/ha). This results may be attributed to the cold weather that occurred in mid-April 2017; with frost on 16 and 17 April 2017. In addition, the weather was not optimal during flower time. On 30 May, a thunderstorm destroyed some flowers. Surprisingly, beekeepers in Germany and the authors of this study observed more than one flowering period in 2017. In some areas in the south and the middle of Germany black locust flowered in mid-June and again at end of August. Further studies and observations are thus needed to get an overview of this behavior and to find mechanism behind it. However, the main flowering period is the end of May and one of the big challenges of flower analysis and honey production of black locust, especially for scientists, but also for non-local beekeepers, is the short time during of the flowering period, which lasts on average 5.5 days [
2]. One week after the main flowering period of
R. pseudoacacia our two beekeepers extracted the honey from their hives. The laboratory of the University of Hohenheim classified the honey as
R. pseudoacacia honey. However, a microscopic pollen investigation revealed 30 different pollens of nectar-giving and nectarless plants in the two honey samples. These results show that, during the flowering time of
R. pseudoacacia, a lot of other plant species produced energy spending flowers or pollen for honeybees. The focus of this study has been on a monoculture (plantation). However, biodiversity and ecosystem health benefits from the presence of many different plant species, and animal species as part of the biocoenosis of the ecosystem. Thus, the analysis of flowers is important to understand ecosystems worldwide.
Natural scientists, researcher, politicians, and layman are constantly learning more about the sensitivity of the environment from the results of UAV-based analyses. UAV technology is a sustainable technology that provides high-resolution analyses and minimal environmental damage. Therefore, the methodology of this study and of other studies [
39,
51,
52,
65] should be further tested to determine whether it is applicable to other tree species of differing ages and in mixed-forest stands. One hypothesis is that older trees probably have a different vertical crown structure (more branch layers) and thus contribute a different proportion of captured flowers. In mixed-forest stands, different tree species are part of the community and have differing flowering times during the year and different flowering-time durations. Flower analysis is difficult in complex ecosystems but provides an interesting indicator of the plant-animal (tree-insect) relationship, ecosystem health, and biodiversity.