Different from most monitoring that only focuses on the species themselves and ignores human impact. By using the gradient boosting decision tree model, the most important factors affecting human utilization were identified, and suggestions on how to effectively control and make decisions were proposed at the management level. Research has found that altitude, maximum vocal pressure level, total vocal duration, and NDVI are the main factors affecting bird richness and activity, while temperature, total vocal duration, maximum vocal pressure level, NDVI, and altitude are the main factors affecting bird activity. Compared to bird richness, selecting parameters can better explain changes in bird activity, which may be related to the fact that activity is a timely feedback of bird behavior choices, adaptability, and quality of life, while richness is more influenced by long-term factors such as habitat suitability and resource abundance. Thus accurately assessing the impact of human noise on bird richness and activity. Therefore, the project has great significance in implementing human impact control on protected areas, and can provide more accurate guidance for the control of protected areas(Figure below). 

By using acoustic event scene detection technology, the richness and activity characteristics of birds were identified. We have achieved higher temporal accuracy (daily, quarterly, annual) and longer time series observation and disclosure of biodiversity characteristics(Figure below). As shown in the figure, it reveals the ranking of bird song heat in different months, the differences in bird richness at different time points of the day, and the continuous song heat map of the orange winged babbler for six months.

The bird list was supplemented through passive acoustic monitoring, and the effectiveness of the monitoring was confirmed. Compared with the ordinary line survey method, passive acoustic methods recognition is more effective. 60 species of birds were identified, belonging to 4 orders and 23 families. Among them, the identification results included 3 species of national second-class protected animals, namely the big noisy babbler, the orange winged noisy babbler, and the red billed sparrow. 

Advancements in on-board technologies and AI integration hold great potential to further enhance the existing drone-based crocodilian monitoring method. Improvements in drone hardware, such as hybrid models with extended flight times and enhanced camera resolutions, allow for broader habitat coverage and the capture of more detailed imagery in complex environments. Integrating artificial intelligence (AI) represents a significant opportunity to streamline image analysis by automating crocodile detection and size estimation using allometric models. These AI-driven enhancements could provide near real-time data processing, reducing reliance on time consuming manual analysis.

This improvements are currently under development with my collaborators

This building block on capacity building on local stakeholders including Indigenous including Peoples and Local Communities (IPLCs) to operate drones, enabling them to take active roles in conservation. 

Ease of Use of the method devellopted:

1. Minimal Technical Skills Required:
   Users only need basic training in drone operation and measurement extraction from high-resolution images. The process is straightforward:
   • Fly the drone following the standardized flight protocol.
   • Marke crocodilians on overhead images.
   • Measure the visible head length using accessible image analysis tools (e.g., ImageJ, QGIS).
   • Apply the corresponding allometric equation or lookup from pre-prepared tables (abaques) for total length estimation.
2. Readily Adaptable:
   The framework uses easy-to-read tables (abaques), making it accessible to both specilialists and non-specialists for operators can quickly apply the method without requiring advanced scientific expertise.
3. Accessible Equipment:
   The approach relies on consumer-grade drones and widely available software, ensuring affordability and reducing barriers to adoption.

Why It’s Effective:

The framework’s simplicity, scalability, and reliability make it ideal for diverse contexts, from remote wetlands to urban-adjacent habitats. It empowers a broad range of users to generate scientifically robust data.

The allometric framework is a non-invasive tool designed to estimate the total body length of crocodilians based on the measurement of their head length, captured through high-resolution drone imagery. By leveraging established species-specific head-to-body length ratios, this method eliminates the need for physical capture or handling, reducing risks for both researchers and wildlife. Validated for 17 of the 27 crocodilian species, the framework allow to provides reliable demographic data essential for population monitoring and conservation management.

The framework uses easy-to-read tables (abaques), making it accessible to non-specialists, operators can quickly apply the method without requiring advanced scientific expertise.

Understanding the demographic structure is vital for wildlife research and conservation. For crocodylians, accurately estimating total length and demographic class usually necessitates close observation or capture, often of partially immersed individuals, leading to potential imprecision and risk. Drone technology offers a bias-free, safer alternative for classification. This study evaluated the effectiveness of drone photos combined with head length allometric relationships to estimate total length, and propose a standardized method for drone-based crocodylian demographic classification. 

This building block establishes standardized flight parameters for effective crocodilian monitoring