Elsevier

Ecological Indicators

Volume 75, April 2017, Pages 95-100
Ecological Indicators

Automatic identification of rainfall in acoustic recordings

https://doi.org/10.1016/j.ecolind.2016.12.018Get rights and content

Highlights

  • We propose a method for the automatic detection of rainfall by using acoustic recordings.

  • The method is an indicator of the rainfall intensity in a period of time.

  • We compare the results of our method with human auditory identification and data provided by a pluviometer.

Abstract

The rainfall regime is one of the main abiotic components that can cause modifications in the breeding activity of animal species. It has a direct effect on the environmental conditions, and acts as a modifier of the landscape and soundscape. Variations in water quality and acidity, flooding, erosion, and sound distortion are usually related with the presence of rain. Thereby, ecological studies in populations and communities would benefit from improvements in the estimation of rainfall patterns throughout space and time.

In this paper, a method for automatic detection of rainfall in forests by using acoustic recordings is proposed. This approach is based on the estimation of the mean value and signal to noise ratio of the power spectral density in the frequency band in which the sound of the raindrops falling over the vegetation layers of the forest is more prominent (i.e. 600–1200 Hz). The results of this method were compared with human auditory identification and data provided by a pluviometer. We achieved a correlation of 95.23% between the data provided by the pluviometer and the predictions of a regression model. Furthermore, we attained a general accuracy between 92.90% and 99.98% when identifying different intensity levels of rainfall on recordings.

Nowadays, passive monitoring recorders have been extensively used to study of acoustic-based breeding processes of several animal species. Our method uses the signals acquired by these recorders in order to identify and quantify rainfall events in short and long time spans. The proposed approach will automatically provide information about the rainfall patterns experienced by target species based on audio recordings.

Introduction

Among climatic processes, the rainfall is one of the main abiotic factors influencing the temporal patterns of reproductive activity in tropical and temperate animal species (Birkett et al., 2012, Gottsberger and Gruber, 2004, Keast and Marshall, 1954). The rain modifies the physical properties of the breeding sites, by adding water, and increases the relative humidity of the environment (Busby and Brecheisen, 1997, Ladányi et al., 2015). As a result, it stimulates or discourages reproductive behavior, which could produce modifications in the animal communication (Amézquita and Hödl, 2004, Lack, 1950, Zina and Haddad, 2005), changes in population dynamics (Georgiadis et al., 2003, Mondet et al., 2005, Ogutu and Owen-Smith, 2003), and shifts in phenology (Primack et al., 2009, Saenz et al., 2006, Yoo and Jang, 2012, van der Kolk et al., 2016).

Developing studies to estimate instant rainfall effects in animal sound production is a burdensome task. In general, weather stations are not necessarily located near the study area (Carey and Alexander, 2003, Mendelsohn et al., 2007), i.e., they are far from the site to be representative of the local weather conditions experienced by the community (Carey and Alexander, 2003). This implies that the correlation of rainfall data with the sounds produced by populations with narrow distribution is not always accurate. Additionally, the rainfall is normally measured in daily and hourly rates by weather stations (Ban and Schmidli, 2015, Hidalgo et al., 2014, Meek and Hatfield, 1994, Du et al., 2016), despite rain can disrupt or encourage animal auditory communication in shorter temporal spans (Lack, 1950, Zina and Haddad, 2005).

Automatic acoustic recorders have been extensively used in passive monitoring of animal species (Bedoya et al., 2014, Busby and Brecheisen, 1997, Depraetere et al., 2012, Farina, 2014, Gregory and van Strien, 2010, Kalan et al., 2015, Laiolo, 2010, Pace et al., 2010, Pieretti et al., 2011, Towsey et al., 2014, Sueur et al., 2008). These devices acquire the sounds of the landscape regardless the nature of their source; therefore, the collected recordings could be used to identify sounds of abiotic sources (e.g. rainfall) in terrestrial and aquatic ecosystems. The rainfall produces one of the most recognizable, and variable, sounds in nature; however, the detection of rainfall events in ecoacoustic studies (Sueur and Farina, 2015) has been barely studied.

Most of the current acoustic methods for rainfall identification have been proposed for signals collected with hydrophones in marine ecosystems (Amitai and Nystuen, 2008, Ma and Nystuen, 2005, Medwin et al., 1992). Although the sound of rainfall in aquatic and terrestrial ecosystems presents several similarities, they are generated under different circumstances. The underwater ambient sound generated by rainfall is a consequence of raindrops colliding with the water surface and trapping bubbles (Amitai and Nystuen, 2008). In a forest, or other landscapes, the sound of the rainfall is produced by the impact of raindrops on the vegetation layers (mainly in the canopy). In aquatic and terrestrial environments the rainfall sound is present throughout the audible spectrum with heterogeneous power in specific frequency bands. Nonetheless, sensors, soundscapes, and power distribution of the rainfall sound across the spectrum differ from both environments. For these reasons, methodologies developed for analyzing underwater rain sound cannot be directly extrapolated to the terrestrial case.

Few studies have been focused on rainfall identification in acoustic recordings of terrestrial ecosystems. To the best of our knowledge, the method proposed by Ferroudj et al. (2014) was the first and only known approach to solve this issue. They proposed a pattern recognition approach with the use of 5 features (temporal entropy, spectral entropy, acoustic complexity index, background noise, and spectral cover) and a decision tree for a bi-class classification problem (heavy rain or non-rain), obtaining an overall accuracy of 93%.

The aim of our study is to provide a tool for detecting, quantifying, and analyzing rainfall events in acoustic recordings obtained from evergreen forests. Thereby, short- and long-term effects of rainfalls in the animal sound production can be analyzed. This approach illustrates the potential for studies related with changes in phenology, auditory communication, or population dynamics as a result of changing rainfall conditions, by solely using acoustic signals provided by automatic recorders.

Section snippets

Rainfall detection

The rainfall produces spectral components throughout the audible spectrum (the sound produced by the impact of each raindrop depends on its size and the material of the receptor). For this reason, the rainfall can be seen in the spectrogram as a background sound (similar to the static television noise). However, the power of this sound is not uniformly distributed across the spectrum. This effect is less visible in the spectrogram, but it is evident in the power spectral density (PSD).

Fig. 1

Experimental results and discussion

In the identification of rainfall – non-rainfall recordings of all levels of intensity (light, moderate, heavy, and violent), the method attained an overall accuracy of 92.9% (Tmean = 0.9 × 10−6, Tsnr = 1.8) (see Table 1). This is because light intensity events also contained drizzles. These drizzles were slightly audible, and they could not be measured by the pluviometer (i.e. the amount of rainfall was inferior to the measurement error, ± 0.04 mm). For this reason, the accuracy reached a value of

Conclusions

In this paper, we propose a method to infer the rainfall intensity levels directly from acoustic recordings. This avoids the use of additional devices for acquisition of rainfall information (e.g. pluviometer) by using an already existent infrastructure of passive monitoring acoustic recorders. Additionally, this procedure allows directly observing local weather conditions experienced by the community that is being acoustically monitored.

The power spectral density (PSD) is a good estimator of

Conflict of interests

There is not any relationship between the manufacturer of the recorders used for this research and the authors of this paper. The corresponding author was affiliated to Universidad de Antioquia during the development of this research.

Acknowledgements

This project was supported by “Fondo de Sostenibilidad Universidad de Antioquia – Estrategia de sostenibilidad”, and “Isagen – contract 47/574”.

References (44)

  • M. Towsey et al.

    The use of acoustic indices to determine avian species richness in audio-recordings of the environment

    Ecol. Inf.

    (2014)
  • H.-J. van der Kolk et al.

    Using a phenological network to assess weather influences on first appearance of butterflies in the Netherlands

    Ecol. Indic.

    (2016)
  • A. Amézquita et al.

    How, when, and where to perform visual displays: the case of the Amazonian frog Hyla parviceps

    Herpetologica

    (2004)
  • E. Amitai et al.

    Underwater acoustic measurements of rainfall

    Precipitation: Advances in Measurement, Estimation and Prediction

    (2008)
  • N. Ban et al.

    Heavy precipitation in a changing climate: Does short-term summer precipitation increase faster?

    Geophys. Res. Lett.

    (2015)
  • P.J. Birkett et al.

    Animal perception of seasonal thresholds: changes in elephant movement in relation to rainfall patterns

    PLoS ONE

    (2012)
  • W. Busby et al.

    Chorusing phenology and habitat associations of the crawfish frog, Rana areolata (Anura: Ranidae), in Kansas

    Southwest. Nat.

    (1997)
  • J.T. Bushberg

    The Essential Physics of Medical Imaging

    (2002)
  • C. Carey et al.

    Climate change and amphibian declines: is there a link?

    Divers. Distrib.

    (2003)
  • A. Farina

    Soundscape Ecology: Principles, Patterns, Methods and application

    (2014)
  • M. Ferroudj et al.

    Detection of rain in acoustic recordings of the environment. PRICAI 2014: Trends in Artificial Intelligence

    Lect. Notes Comput. Sci.

    (2014)
  • N. Georgiadis et al.

    The influence of rainfall on zebra population dynamics: implications for management

    J. Appl. Ecol.

    (2003)
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