Tech and Innovation
Did you know that data quality is one of the main factors determining the credibility of an environmental program? According to the IPCC's Good Practice Guidance for Land Use, Land-Use Change and Forestry, the environmental monitoring process depends heavily on data that is consistent, transparent, and verifiable.
In tree planting projects, data plays an important role in answering various fundamental questions. How many trees were planted? Where did the planting take place? Are those trees growing well after some time?
All of this information becomes part of the Monitoring, Reporting, and Verification (MRV) process. Without accurate data, the monitoring process cannot reflect the actual conditions in the field.
That is why data quality is the main foundation for maintaining the transparency and credibility of tree planting projects. When the data collected is accurate and consistent, the resulting reports are also more trustworthy for various stakeholders, from the implementing organization to the parties supporting the program.
Data collection in the field does not always go perfectly. Various factors can affect the quality of the data collected by the monitoring team.
Recording errors are one example that occurs quite often. Field officers may enter the wrong number of trees or incorrectly record planting location coordinates. On a small scale, these errors may seem insignificant. However, when data is collected from hundreds to thousands of planting points, small errors can grow into large data discrepancies.
Besides technical errors, bias can also arise in the reporting process. For example, the reported data tends to show only results that look good, while trees that are not growing optimally are less documented.
In certain situations, data manipulation can also potentially occur. Without a good control system, the number of trees or the success rate of planting can be reported higher than the actual condition.
This is why a good data management system needs to be equipped with a mechanism capable of detecting irregularities early.
According to the World Resources Institute, good data quality requires a verification and control process capable of identifying discrepancies before the data is used in decision-making.
In the context of tree planting projects, detecting anomalies early is very important so that potential errors can be corrected promptly. When the system is able to identify unusual data, the management team can conduct a re-check before the data enters the official report.
For example, if a location is reported to have a number of trees far higher than the average of other locations with the same area, the system can flag that data as a potential anomaly.
This flagging does not mean the data is definitely wrong. However, it serves as a signal that the data needs further examination to ensure its accuracy.
With this approach, errors can be corrected earlier before they affect the entire project report.
In managing complex environmental data, the oversight process does not always have to check all data manually. A risk-based approach allows the system to prioritize data with a higher potential for discrepancy.
This is where the concept of anomaly detection plays an important role. This technology helps identify unusual data patterns compared to the normal patterns established previously.
For example, the system can detect the following conditions:
When the system finds an unusual pattern, that data will be flagged for an additional verification process.
This approach helps the project management team focus attention on high-risk data without having to check the entire dataset manually.
Simply put, an anomaly can be interpreted as data that deviates from the normal pattern it should follow.
In tree planting projects, the normal pattern is usually formed from various parameters such as the number of trees per area, planting spacing, reporting time, and the characteristics of the planting location.
When there is data that differs significantly from that pattern, the system can identify it as a potential anomaly.
For example, if most planting locations record 200 to 300 trees per hectare, and then one report records 1,000 trees per hectare, that data can be categorized as an anomaly.
A situation like this does not always mean an error. However, the anomaly becomes an important indicator that the data needs further examination so the report remains accurate.
In managing tree planting projects that involve many locations and field data, checking all data manually is not an efficient approach.
A risk-based approach helps the management team prioritize data with a higher potential for problems.
By using an anomaly detection system, the team can more quickly find data that needs re-verification. This approach helps improve the efficiency of the monitoring process while maintaining the quality of the resulting reports.
In the end, anomaly detection is not only about finding errors in data. More than that, this approach helps maintain the integrity of the information that forms the basis for evaluating the success of a tree planting program.
When data quality is maintained, the credibility of the project also grows stronger. This is important to ensure that the environmental recovery efforts carried out truly deliver a real impact for the ecosystem and society.
Jejakin’s green programs combine high-tech monitoring, biodiversity restoration, and community-led initiatives to deliver powerful, sustainable change across ecosystems.
