Creating a Sample Tree Condition Survey in Mapit GIS - Design Stage
Introduction
Tree condition surveys are a core task for local authorities, consultants, and land managers. They support asset management, risk assessment, maintenance planning, and public engagement. Mapit GIS provides a lightweight, field-friendly platform for collecting spatial survey data using mobile devices and web maps.
This article walks through how to design and implement a sample tree condition survey in Mapit GIS, focusing on best practice rather than a single rigid configuration. The principles can be adapted for different tasks like inspections, highway tree inventories, assets collection etc.
Project Management - Best Practice
Projects are the primary organisational unit within Mapit GIS Professional and should be used as the standard mechanism for managing survey and spatial data. Each project is stored in OGC GeoPackage (GPKG) format, providing a standards-compliant, portable container for spatial layers, attributes, and associated configuration. Projects enable clear separation of datasets, controlled data handling, and consistent application of settings across different surveys or workstreams.
Projects support full import and export functionality, allowing them to be treated as discrete data assets. Exported projects can be securely backed up to local or network storage, transferred between devices, or archived in accordance with organisational data retention policies. Users may create, switch between, and remove projects as required, enabling flexible working while maintaining strong data governance controls.
Use one project per survey or work package.
This approach reduces the risk of data contamination between datasets and simplifies backup, audit, and handover processes.
Desktop GIS Compatibility
Because projects are stored as GeoPackage files, they can be opened directly in desktop GIS software such as QGIS or ArcGIS for inspection, analysis, or reporting. This supports downstream workflows such as spatial analysis, cartographic outputs, and integration with other corporate datasets.
Direct editing of a project GeoPackage in desktop GIS software is not recommended if the data is intended to be re-imported into Mapit GIS Professional. Changes to layer schemas, field definitions, or metadata may result in incompatibility.
For post-processing and analysis, always work on a copy of the exported GeoPackage.
Retain the original exported project unchanged to ensure it can be safely re-imported if required.
Project Removal and Data Retention
Removing a project from Mapit GIS Professional permanently deletes all associated layers and survey data from the application. This operation is irreversible, although users are prompted for confirmation prior to deletion.
Before removing a project, ensure that any required data has been backed up using the Export Project function available in the Project Options menu. Failure to do so may result in permanent data loss.
Exported projects may be re-imported into Mapit GIS Professional at a later date, supporting recovery, audit, or continuation of work.
Attachment Handling
When a project is removed, underlying attachment files (such as photographs) are not automatically deleted from device storage. Instead, the associated attachment directory is renamed using the format: projectname_timestamp.
This behaviour allows for potential recovery if required.
Attachment folders must be reviewed and removed manually if permanent deletion is required, in line with organisational storage and data protection policies.
1. Define the Purpose of the Survey
Before configuring anything in Mapit GIS, clearly define what the survey needs to achieve. This will drive both your data model and your field workflow.
Typical objectives include:
- Recording tree locations and basic attributes
- Assessing health and structural condition
- Identifying maintenance requirements
- Prioritising risk or intervention
- Creating a baseline inventory for future comparison
2. Design the Tree Data Model
A well-defined data model is essential for ensuring consistency, data quality, and long-term usability of tree survey data. The tree data model should be designed before field collection begins and aligned with the survey objectives, organisational standards, and anticipated downstream analysis.
In Mapit GIS Professional, tree surveys are typically represented as point features, with each point corresponding to an individual tree. Attributes should be structured to minimise free-text entry, maximise use of controlled vocabularies, and support repeatable data capture across surveyors and projects.
2.1 Spatial Representation
- Feature type: Point
- Feature description: Individual tree location
- Capture method: GPS capture or map-based point placement
- Coordinate reference system: Defined by project settings (inherited from GeoPackage)
Capture tree locations as close to the stem base as practicable to support accurate mapping and future re-surveys.
2.2 Attribute Grouping Principles
Attributes should be logically grouped to reflect the survey workflow and improve usability in the field. The following groups are recommended:
- Identification
- Size and structure
- Condition assessment
- Risk and management
- Supporting information
This structure also supports clearer analysis and reporting once data is exported.
2.3 Identification Attributes
These attributes uniquely identify each tree and support cross-referencing with external systems.
| Attribute | Data Type | Description |
|---|---|---|
| Tree ID / Barcode | BARCODE | Unique identifier or asset tag |
| Species | TEXT (dropdown) | Tree species |
| Common Name | TEXT | Optional descriptive name |
Using barcode or QR code identifiers improves accuracy and speeds up repeat inspections.
2.4 Size and Structure Attributes
These attributes describe the physical characteristics of the tree using banded values rather than exact measurements.
| Attribute | Data Type | Description |
|---|---|---|
| Height Class | TEXT (dropdown) | Height band (e.g. below 5m, 5-10 m) |
| DBH Band | TEXT (dropdown) | Diameter at breast height band |
| Crown Spread | TEXT | Optional estimate or class |
Using classes rather than precise measurements reduces subjectivity and improves consistency between surveyors.
2.5 Condition Assessment Attributes
Condition attributes should be based on clearly defined categories and supported by guidance notes where necessary.
| Attribute | Data Type | Description |
|---|---|---|
| Overall Condition | TEXT (dropdown) | General tree condition |
| Structural Defects | TEXT (multi-select) | Observed defects |
| Physiological Condition | TEXT (dropdown) | Optional vitality assessment |
Ensure that condition categories are clearly defined and understood by all surveyors to maintain data consistency.
2.6 Risk and Management Attributes
These attributes support prioritisation and decision-making.
| Attribute | Data Type | Description |
|---|---|---|
| Risk Category | TEXT (dropdown) | Assessed risk level |
| Recommended Action | TEXT (dropdown) | Management action |
| Action Priority | TEXT (dropdown) | Timescale for intervention |
Risk-related attributes should not be left blank. Where uncertainty exists, use agreed organisational defaults or escalation procedures.
2.7 Supporting Information
Additional contextual information can be captured to support interpretation and audit.
| Attribute | Data Type | Description |
|---|---|---|
| Notes | TEXT | Free-text observations |
| Photographs | ATTACHMENT | Supporting images |
Photographs provide valuable evidence for condition assessments and should be captured consistently. Photographs or other type of attachments are associated with a specific feature however they are stored on the device memory and they are not part of the spatial layer.
2.8 Data Quality and Replicability Considerations
To support scalable and repeatable surveys:
- Use dropdowns and multi-select lists wherever possible
- Avoid free-text fields for critical attributes
- Keep attribute names stable across projects
- Document attribute definitions centrally
This approach ensures that tree survey data remains comparable over time and across multiple projects or teams.