Transparency means that something is done in an open way without secrets. When it comes to land-use monitoring, transparency means that all activities are completely and openly documented so that they can be understood and potentially reproduced. These transparent practices apply to the collection, processing and storage of data.

It is important to be transparent on where data comes from, and there may be limitations to data interpretation if they were generated for a specific purpose. For example, if a country monitors forests but only for one region, the information cannot be used for national interpretations.

Clarity means that information can be easily understood. Information is presented in a straightforward and unambiguous way, that avoids confusion and misinterpretation.

For monitoring of land use, clarity ensures that data and results are effectively presented to stakeholders, who can comprehend them without difficulty.

Accuracy describes how close a measurement is to the real value. Striving for accuracy means using methods that deliver estimates that are neither under- nor over-estimated.

In the context of land-use monitoring, estimates are accurate if they are close to the true emissions and removals that the atmosphere sees or the land use that can be observed on the ground.

Achieving high accuracy requires using methods and data that correspond with the local conditions. It is also important to be precise, which means to produce results with low variability.

Accuracy can only be achieved to some degree. Methods and data applied for land use monitoring will always deviate from the true value.

Transparent monitoring approaches require that uncertainty is clearly communicated.

Users of data and information need to be able to assess the accuracy of an estimate. They can do this if they receive information on how much a value can vary around the estimate. Sometimes a qualitative description of uncertainty is easier to understand for stakeholders.

Consistency means that data collection, processing, and analysis is uniform and stable over time.

For example, to estimate GHG emissions from forest loss over time, the same methods for interpreting different satellite images should be used each time.

Consistency requires using common methodologies like the IPCC GHG inventory methods and guidelines. This allows users to compare estimates of different countries, despite different data sources and conditions.

Consistency does not mean that new technologies or data cannot be used. But if methods and data change, these changes should also be applied to earlier emissions estimates to make them all comparable.

Completeness means that all relevant information that has an impact on GHG emissions and removals from land is included.   

This means providing estimates for all relevant carbon pools — for example, carbon in tree biomass, soils, and wood products — and including all relevant areas, land use activities, and greenhouse gases.

Comparability is the ability to compare data and results from different data sources or studies, including across regions and time periods.

Comparability is closely related to consistency. Consistent methods over time and space ensure comparability between estimates for different periods and regions.

Achieving comparability in land-use monitoring requires standardization. This ensures that land-use categories, measurement techniques, and units of measurement are the same across different datasets.

Interoperability means that different systems, datasets, and organizations can work together seamlessly.

In land-use monitoring, interoperability is achieved when data from various sources, such as remote sensing data, field surveys, and historical records can be integrated and used. This often involves using common data formats and procedures for data sharing.

Concretely, providing data about the data (also known as metadata), helps users with understanding and interpretation. When organizations collaborate, they should share their needs from the beginning so that data can be used for multiple purposes.

Complementarity means that different types of data, methods, and systems can be combined to provide a more complete and accurate picture. 

In land-use monitoring, increasing complementarity helps to overcome limitations of single methods and to enhance the overall quality of monitoring. 

Concretely, combining various data sources such as satellite imagery, aerial photography and field surveys helps to exploit their individual strengths. The result is a comprehensive and more robust dataset. Data must be interoperable to be complementary.

Scale refers to the level of spatial and temporal resolution at which data is collected and analyzed. The scale determines results and what conclusions can be drawn from the data. 

In land-use monitoring, scalability of data helps to understand land-use processes at different levels.  

Concretely, fine scale data that provides detailed information about specific sites is useful for management and planning. For example, data on individual farms.  In other cases, coarse scale data is more useful, for example when there is a need for data to cover the entire country to support the development of policies and measures. 

Reproducibility means that all the steps of monitoring land use and calculating emissions or removals from land are documented in a way so that anybody can reproduce the findings and arrive at the same results. 

Reproducibility requires uniform procedures for calculation and adequately trained staff. 

This also requires that different teams understand each other’s work and have a shared understanding of the task. For example, the remote sensing team and the team compiling the GHG inventory need to communicate. 

Adaptability means that the tools and methods used for monitoring land use and calculating emissions or removals from land can be adapted to a user’s needs. 

Reproducibility and a sound understanding of applied steps allows stakeholders to adapt methods to their needs and obtain meaningful results. 

Adaptability also allows learning from experience and improving over time. 

Access aims to ensure that stakeholders can easily find and use the data, tools, methods, and other relevant information on land use monitoring. 

This means that there are easy access points for users and that good documentation, for example manuals, are provided. It also requires training so that users have the capacity to interpret data or apply tools. 

In the context of land use monitoring, it is especially important to improve access to data sets at the national and local scale. 

Distribution means that data and information are disseminated. This requires communication about data and tools — in multiple languages.

Participation means that stakeholders are invited to be involved in monitoring processes and can actively engage in them. 

This means that relevant stakeholders are regularly informed or can contribute with specific tasks, for example, data collection.  

Participation is crucial for ensuring diverse perspectives, fostering collaboration, and achieving inclusive and accepted outcomes. 

Equity means that stakeholders are treated fairly according to their needs, ensuring that everyone has access to the same opportunities and resources. 

In the context of transparent monitoring, equity is provided when institutional arrangements are in place to enforce duties and to protect the rights of stakeholders, for example, through laws that guarantee access to monitoring data or roundtables for discussing methods and results. 

Responsibility refers to the duty to perform a task or role that one is expected to complete. 

In the context of monitoring land use, this means that an individual, a group, or institution is formally in charge of particular tasks within the monitoring process. For example, the collection or storage of data.  

Accountability refers to being liable for the results of a task or decision.   

In the context of land-use monitoring and reporting, it implies that clear roles and outcomes are defined. This ensures that stakeholders are responsible for their results and must explain and justify them. For example, the use of a particular methodology to collect data must be justified and its impact on the results be explained and accounted for.