文献类型：期刊文献

英文题名：Article Diversity Monitoring of Coexisting Birds in Urban Forests by Integrating Spectrograms and Object-Based Image Analysis

作者：Zhao, Yilin[1,2,3] Yan, Jingli[4,5] Jin, Jiali[1,2,3] Sun, Zhenkai[1,2,3] Yin, Luqin[1,2,3] Bai, Zitong[1,2,3,6] Wang, Cheng[1,2,3]

第一作者：Zhao, Yilin

机构：[1] Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China; [2] Key Laboratory of Tree Breeding and Cultivation, National Forestry and Grassland Administration, Beijing, 100091, China; [3] Urban Forest Research Center, National Forestry and Grassland Administration, Beijing, 100091, China; [4] School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, 200240, China; [5] Shanghai Yangtze River Delta Eco-Environmental Change and Management Observation and Research Station, Ministry of Science and Technology, Shanghai, 200240, China; [6] Beijing Institute of Landscape and Traditional Architecture Design and Research Co., Ltd, Beijing, 100005, China

年份：2022

卷号：13

期号：2

外文期刊名：Forests

收录：EI(收录号：20220711643445);Scopus(收录号：2-s2.0-85124570393)

基金：Funding: This research was funded by the National Non-Profit Research Institutions of the Chinese Academy of Forestry (CAFYBB2020ZB008).

语种：英文

外文关键词：Audio recordings - Batch data processing - Biodiversity - Ecology - Forestry - Image analysis - Monitoring - Spectrographs

摘要：In the context of rapid urbanization, urban foresters are actively seeking management monitoring programs that address the challenges of urban biodiversity loss. Passive acoustic monitoring (PAM) has attracted attention because it allows for the collection of data passively, objectively, and continuously across large areas and for extended periods. However, it continues to be a difficult subject due to the massive amount of information that audio recordings contain. Most existing automated analysis methods have limitations in their application in urban areas, with unclear ecological relevance and efficacy. To better support urban forest biodiversity monitoring, we present a novel methodology for automatically extracting bird vocalizations from spectrograms of field audio recordings, integrating object-based classification. We applied this approach to acoustic data from an urban forest in Beijing and achieved an accuracy of 93.55% (±4.78%) in vocalization recognition while requiring less than 1/8 of the time needed for traditional inspection. The difference in efficiency would become more significant as the data size increases because object-based classification allows for batch processing of spectrograms. Using the extracted vocalizations, a series of acoustic and morphological features of bird-vocalization syllables (syllable feature metrics, SFMs) could be calculated to better quantify acoustic events and describe the soundscape. A significant correlation between the SFMs and biodiversity indices was found, with 57% of the variance in species richness, 41% in Shannon’s diversity index and 38% in Simpson’s diversity index being explained by SFMs. Therefore, our proposed method provides an effective complementary tool to existing automated methods for long-term urban forest biodiversity monitoring and conservation. ? 2022 by the authors. Licensee MDPI, Basel, Switzerland.