China Nature Watch: using technology for Biodiversity Information Collection and Application to facilitate biodiversity-friendly decision making

Shan Shui Conservation Center
Published: 25 April 2022
Last edited: 25 April 2022
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Biodiversity baseline data is key to conservation decision-making and practices, yet facing data deficiency and information asymmetry. With the assistance of technology tools, China Nature Watch aims at strengthening the collection of biodiversity data from various sources, especially citizen science, facilitating data applications in land use planning and public participation, and mainstreaming biodiversity conservation. 


Specifically, technology brings effective solutions to 3 modules:

  • Camera trap data management: developing online AI-incorporated data management system to simplify and speed up camera trap data collection and processing.
  • Citizen science data visualization: using Microsoft PowerBI to automatically analyze and interactively visualize species records collected by citizen scientists.
  • Biodiversity Impact Assessment Tool (BiA): integrating ecological and construction data from multiple data sources to provide instant enquiry of biodiversity impact assessment for construction projects via Microsoft Azure.


East Asia
Scale of implementation
Forest ecosystems
Freshwater ecosystems
Grassland ecosystems
Green spaces (parks, gardens, urban forests)
Marine and coastal ecosystems
Pool, lake, pond
River, stream
Salt marsh
Temperate deciduous forest
Tundra or montane grassland
Urban ecosystem and build environment
Urban wetlands
Wetland (swamp, marsh, peatland)
Access and benefit sharing
Biodiversity mainstreaming
Indigenous people
Land management
Local actors
Outreach & communications
Protected and conserved areas governance
Protected and conserved areas management planning
Science and research
Species management
Species Conservation and One Health Interventions
Species Status Assessment
Species Monitoring and Research
Species Conservation Planning
Risk communication, community engagement and behaviour change
Risk assessment
Conflicting uses / cumulative impacts
Lack of public and decision maker’s awareness
Poor governance and participation
Sustainable development goals
SDG 11 – Sustainable cities and communities
SDG 15 – Life on land
Aichi targets
Target 1: Awareness of biodiversity increased
Target 2: Biodiversity values integrated
Target 11: Protected and conserved areas
Target 12: Reducing risk of extinction
Target 17: Biodiversity strategies and action plans
Target 19: Sharing information and knowledge
Business engagement approach
Direct engagement with a company


People's Republic of China


Each process in biodiversity data workflow from collection, processing, visualization, to application is cumbersome and tedious that requires a lot of repetitive labor and is in urgent need of simplification and automatization.

  • Camera trap data management: community-based camera trap monitoring faces bottlenecks of low efficiency (e.g., data collection from local community monitors, manual species identification) and unstable and relatively low data quality (e.g., incorrect and missing data, high rates of blank photos).
  • Citizen science data visualization: visualization products are static and campaign-specific, created manually at several stages of each campaign, which cost much conservationists’ effort while only provide delayed feedbacks to citizen scientists.  
  • Biodiversity impact assessment: data deficiency and lack of public access to data have limited data usage scenarios. Also, biodiversity impact assessment is manually conducted for each enquiry, with reports manually composed.



  • Conservationists: enhanced efficiency
  • Local communities: enhanced efficiency and timely feedback
  • Citizen scientists: timely feedback
  • Government agencies, academic institution, public: easy access to biodiversity data


How do the building blocks interact?

The overall planning and development of supportive partnership have provided concrete foundation for the project.

Camera trap data is an integral species distribution data source for the Nature Watch database. The camera trap data management system speeds up the overall workflow and enhances the timely input of camera trap data into the database. Similar for citizen scientist data, as another species record source, the visualization platform helps inspire citizen scientists’ enthusiasm and boost species observations. Both building blocks accumulate data for the BiA tool, promoting more accurate assessment.

Moreover, the visualization platform and the BiA tool involve public communication targeted at different audience, complementing each other for the ultimate goal of biodiversity mainstreaming.


Technology solutions have optimized biodiversity data workflow and promoted data applications:

  • Camera trap data management: The community-based camera trap monitoring assistant app has made data recording easier for 86 local community monitors in charge of camera trap setup/pickup in field. AI image recognition models have processed over 380,000 camera trap images, substituting more than 100 hours of labor. Such acceleration in data collection and processing enables timely feedback to stakeholders and supports conservation decision-making.
  • Citizen science data visualization: Through visualizing 2688 records of 22 species collected during six campaigns in 2016-2021 and automatically updating newly collected records, the platform has provided spatial, temporal, and interactive feedbacks to citizen scientist participants and significantly boosted interests in nature watch activities.   
  • BiA tool: The Nature Watch database maintains biodiversity baseline data collected from multiple data sources including species records (2591 species, 1.35 million records) and protected areas (6 national parks, 474 national protected areas, etc.). Up till now, the BiA tool has provided interactive and visualized biodiversity impact assessment enquiry services to more than 1260 construction project planners and other stakeholders, facilitating biodiversity-friendly decision making.

Contributed by

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Chunyue Wei Shan Shui Conservation Center

Other contributors

Shan Shui Conservation Center
Shan Shui Conservation Center
Shan Shui Conservation Center
Shan Shui Conservation Center
Shan Shui Conservation Center
Peking University Center for Nature and Society
Xi'an Jiaotong-Liverpool University
Xi'an Jiaotong-Liverpool University
Microsoft Corporation
Huawei Technologies Co., Ltd.