Select data resources

Once the assessment team has selected indicators and metrics for reassessment, the next step is to evaluate available data resources and address any data gaps. Collecting data to measure metrics is often one of the most time-intensive components of a landscape assessment. To streamline this process, the LandScale platform offers a structured workflow and various resources to support users. By documenting all data resources and assessing their limitations on the platform, users can facilitate the assessment and validation processes while preserving valuable information for future reassessments.

Example of data selected for metric 1.1.1.1.

Data source selection for reassessment

For each metric, at least one dataset must be selected. The assessment team will be able to view which data sources were used in the baseline assessment. Whenever possible, the same data source should be used to maintain comparability over time, ensuring the data's timeframe aligns with the reassessment timeframe.

If a different data source is selected for any metric, the assessment team must:

• Provide a clear justification for the change. Acceptable reasons may include improved accuracy, updated methodology, better alignment with assessment objectives, or enhanced data availability.

• Assess and document comparability between the new and original data sources, including any known limitations of the new source.

• Demonstrate how risks of misrepresentation will be mitigated, such as over- or under-estimation, and how the assessment team will ensure trends reflect real-world conditions. This may involve cross-checking results against additional data or contextual landscape knowledge.

If the new data source is not deemed comparable to the original baseline result, the new result will be tagged as a new baseline for that specific metric.

The baseline assessment guidelines provide detailed information on:

• Types of data and data sources.

• Considerations for ownership, privacy, and use.

• Guidance on choosing among competing datasets.

A limitations analysis must be conducted for each selected dataset, even if the same dataset is used for multiple metrics, as limitations may vary by metric purpose. Refer to the baseline assessment guidelines for best practices on analyzing data limitations and addressing data gaps.

Data for future assessments

Documentation of all data investigated and the outcomes of the data limitations analysis within the LandScale platform will enable users to seamlessly avoid unsuitable datasets during reassessments.

However, caution must be exercised when there are significant differences in the characteristics of the data used across assessments. For example, when measuring the amount or rate of ecosystem conversion, newer data with improved precision may introduce apparent increases or decreases that result from methodological improvements rather than actual landscape changes. This could lead to significant over- or under-estimation of true conversion rates.

It is also important to identify whether a metric definition has changed (e.g., between LandScale framework versions) to ensure that the correct data is used for the specified measurement. If a metric definition has changed, the assessment team may propose using the original metric as an alternative to maintain continuity and comparability of results across assessments, rather than adopting the new metric definition.

Determine comparability of selected data

Ensuring data comparability and coherence is essential for producing reliable insights and meaningful trend analyses. Comparability refers to the extent to which data align across different time periods, sources, or contexts.

Achieving comparability requires aligning definitions, measurement techniques, and methodologies across datasets. If direct alignment is not possible, statistical techniques such as normalization, standardization, or data transformation may be necessary to adjust for differences in scale, units, or collection methods.

Where feasible, efforts should be made to directly align datasets. If alignment is not possible, adjustments should be documented transparently, specifying the methodologies used and any resulting limitations.

In cases where a new dataset or methodology significantly improves accuracy or detail, it may be necessary to establish a new baseline from the point of change. This approach preserves historical context while maintaining the integrity of future comparisons. All modifications, including reasons for methodological shifts and potential impacts on interpretation, should be clearly documented to ensure transparency and facilitate consistent long-term analysis.

Considerations for geospatial data (primary or secondary)

Geospatial data plays a critical role in LandScale assessments. When using geospatial data, assessment teams should evaluate the following factors:

Factor	Description
Source	Confirm consistency in dataset origin and justify if sources change.
Definition	Ensure datasets describe the same phenomena (e.g., "forest cover" vs. "forest extent").
Spatial and temporal resolution	Match resolution wherever possible. If adjustments are needed, apply systematic down-sampling or up-sampling.
Temporal adjustments	Justify differences in timeframes, such as using monthly versus annual averages.
Units	Maintain consistency in measurement units; apply normalization or conversion if necessary.
Projections	Ensure data use the same projection; re-projection may be required.
Disaggregation and aggregation factors	Maintain consistency in classification levels (e.g., land cover categories, crop types). If merging data, ensure groupings are methodologically sound.

Considerations for survey data (primary or secondary)

When using survey data, assessment teams should evaluate the following factors:

Factor	Description
Spatial scale	Ensure the spatial scale of participants remains consistent across the landscape (e.g., selecting participants from the same regions or adjusting sampling techniques appropriately).
Temporal scale	Conduct data collection at similar times of the year to account for seasonal variations.
Sample size and demographics	Maintain a similar number of participants and match demographic breakdowns to the baseline. Subsampling or weighting adjustments may be necessary.
Survey questions	Ensure consistency in wording, response scales, and data collection methods.
Units	Maintain consistency in measurement units; apply normalization or conversion as needed.
Statistical analysis	Apply the same statistical methods for downstream calculations and indices. Provide justifications for any differences.

Handling unchanged data sources

If the data source used in the baseline assessment has not been updated since then, the following options apply:

• Exclude from reassessment: Mark the metric as 'data deficient' if no updates are available to exclude it from reassessment.

• Qualitative update: Supplement missing quantitative data with contextual information or qualitative observations.

• Recalculate using an alternate dataset: Use a comparable dataset and document any limitations in its comparability (e.g., differences in resolution, geographic coverage, temporal gaps).

Recommended stakeholder input

Engaging with landscape stakeholders can significantly enhance the data collection process for a LandScale assessment. Stakeholders often possess valuable insights or direct access to relevant datasets. To maximize efficiency and data quality, assessment teams are advised to:

1. Identify and consult stakeholders early: Begin consultations as early as possible to accommodate the lead time often needed to procure existing datasets. Data-related consultations may be integrated into earlier stakeholder engagement activities or conducted as standalone bilateral or group discussions.

2. Document findings on the platform: Data identified through stakeholder engagement can be logged on the LandScale platform at any stage. This ensures early integration and efficient tracking of potential data resources.

Stakeholder groups who may be especially relevant to consult about data include:

• Data developers and data users: Entities such as government agencies, research institutions, and NGOs frequently utilize datasets similar to those required for LandScale assessments. These organizations may have already conducted thorough data searches and evaluations to support planning and assessment needs. For example, a municipal land-use planning agency may have curated data for land use/land cover, ecological and hydrological features, and social factors.

• Companies operating in the landscape: Companies involved in producing or sourcing commodities often collect extensive data on production systems and producers. While such data is typically proprietary, these companies can provide valuable insights into specific metrics or indicators.

By engaging stakeholders early, assessment teams can broaden access to critical datasets, foster collaboration, strengthen assessment credibility, and better align data collection processes with the landscape's unique context and needs.