Using Open Foris Collect in National Forest Inventory of Bangladesh: ensuring a robust and efficient data management system

A success story of using Open Foris Collect in National Forest Inventory

Mondal Falgoonee Kumar

Acknowledgements

It is a great opportunity to work with the Food and Agriculture Organization (FAO) of the United Nations in Bangladesh and become a part of the National Forest Inventory (NFI) of Bangladesh. The Bangladesh Forest Inventory (BFI) established the baseline for developing a sustainable forest monitoring system for the Forest Department. I would like to thank the Forest Department, FAO, United States Forest Service (USFS), Silvacarbon, Universities (e.g. Khulna University, Chittagong University, Shahjalal University of Science and Technology, Dhaka University) and each other contributing organizations to make this possible.

Open Foris Collect developed by FAO was successfully used for data collection, management and reporting purposes in BFI. For making this possible several people have contributed significantly especially- Liam Costello, Rajib Mahamud, Purnata Chakma, Zaheer Iqbal, Touhidor Rahaman, Nikhil Chakma, Kristofer Johnson and Matieu Henry. I would like to recall the contribution of all field crews in field data collection precisely using Open Foris Collect Mobile. I am grateful for all the support to establish a data management system using Open Foris tools during the inventory.

Special thanks goes to the Open Foris team in FAO, especially to Stefano Ricci (FAO) for his training on Open Foris data management and prompt response in solving related issues. Along with Stefano Ricci I would sincerely acknowledge Alfonso Sanchez-Paus (FAO) and Lauri Vesa (FAO) for their genuine contribution to prepare this report. This report will be a good example of successfully using collect in NFI data collection and management.

1. Introduction

National forest inventory gathers, produces and disseminates robust information of the country’s forest resources to decision makers and stakeholders to support National Forest Monitoring and Assessment (FFMA) (AIMS, 2016). To establish a well-functioning, fully operational and regular monitoring system to obtain data on changing public demand on forest and analyzing the status of tree and forest resources of Bangladesh, the Forest Department (FD) under the Ministry of Environment, Forest and Climate Change (MoEFCC) started the National Forest Inventory (NFI) of Bangladesh in 2016. This continuous process is known as the Bangladesh Forest Inventory (BFI).

The BFI has been equipped with the latest technologies in the field of forest inventory. New and updated equipment in field measurement and Free and Open Source (FOSS) technologies for data collection, management and analysis have made the BFI a timely updated forest inventory example to follow in this region. A major key to success of the BFI has been the use of Open Foris Collect and Collect Mobile to gather, manage and disseminate national scale data from the field.

2. Objectives

The goal of this publication is to provide a guideline to the countries using Open Foris Collect in National Forest Inventory data collection and management. Specific objectives are-

1. To provide an idea for forest inventory data collection and management using Open Foris Collect
2. Share the process of data robustness in Open Foris Collect

3. Open Foris tools

Open Foris is a collective set of open sources software tools designed for forest inventories. With broad experience of FAO in forest monitoring and assessment and to provide support to countries in National Forest Monitoring and Assessment this set of tools were originally developed under the FAO-Finland Forestry program (AIMS, 2016). The original set of tools available in Open Foris were as follows:

1) Open Foris Collect

2) Open Foris Collect Mobile

3) Open Foris Collect Earth

4) Open Foris Calc

5) Open Foris Geospatial Toolkit

Later, with support of Norway, the SEPAL platform was developed for satellite image processing in the cloud and the Geospatial Toolkit was merged into it. NASA-Servir has added Open Foris Collect Earth in the Open Foris suite, and the latest tool is EarthMap for making efficient multi-temporal queries via Google Earth Engine.

4. Background of NFI of Bangladesh

Several forest inventories were done by Bangladesh forest department but none of those covered the complete aspect of the country’s tree and forest resources. Moreover, the problems of identifying previous cycles’ inventory plots and continuously changing nature of natural resources dynamics provided valid reasons to go for a complete new national level inventory design rather than to re-measure the old inventory plots (Costello et al. 2016).

Considering the objectives, geographical and social context, tree and land tenure and forest management mandate the county forests are classified into five zones for inventory (BFD, 2016). With the result of expert workshops, national consultations and meetings, the design of the new BFI was finalized (BFD, 2016). A total of 1858 plots were identified throughout the country for collecting field inventory data distributed among the five zones (BFD, 2016). Total of 12 teams were trained to collect field data and four teams were responsible to ensure the quality of data gathered by the field teams (BFD, 2016). Besides biophysical data, for the first-time socio-economic context were also incorporated in forest inventory to sketch the dependency of people on forest (BFD, 2017). Considering different factors and calculation a total of 6400 households were selected from 320 administrative unions (GOB, 2017).

5. Overview of biophysical survey design and components

5.1. Zoning

Bangladesh contains diverse forest types and ecosystems. Considering different biotic and abiotic characteristics (such as- vegetation, soil, rainfall, topography, ownership, land use etc.) major forests are categorized as different zones. Village zone is a new category that includes social forestry, household forest resources, institutional plantations, roadside plantation etc. Zoning is a convenient way to report results and serve statistical purposes with higher precision of forest attribute estimates (BFD 2016). Depending on zones, plot design varies (plot size and subplot number changes) for data collection. In the BFI the established five zones (GOB, 2019) are given in the table below-

5.2. Plot design

An inventory plot of BFI consists of five subplots except for Sundarbans and Coastal zone (three subplots for this zone’s plots) (BFD, 2016). Considering the accessibility, workload, amount of information this was designed to attain the desired precision level. Each sub-plot contains three different level plots within. Different plots provide specific information for record. Large plot (L plot) provides tree attributes data, Medium plot (M plot) provides sapling measurements and Small plot (S plot) gives soil seedling information. Besides there are specific locations for soil and litter sample collection, down wood materials information (GOB, 2019). Check the below diagram for a clear sketch of the plot design-

Figure 1: Plot design sketch of BFI

5.3. Major components for data analysis reporting

Bangladesh Forest Inventory (BFI) was designed to provide robust information about tree and forest resources, so that it can provide the base of a sustainable forest monitoring system. Besides, related socioeconomic attributes were also measured to predict the relationship between human and forest resources. People dependency was framed to consider public demand and dimension for forest related policy formation. However, the major components of BFI (GOB, 2019) data collection, management and analysis are-

1. Extent of tree and forest resources and their changes
2. Biodiversity and their conservation status
3. Growing stock, biomass and carbon estimation
4. Management and ownership of forests
5. Disturbances to tree and forests
6. Support for sustainable forest management
7. Interaction of tree and forest services with livelihood

6. Survey design using Open Foris Collect

Open Foris Collect supports diverse data types, complex validation rules and can handle data collection in different formats like- form view, tabular view etc. BFI biophysical and socio-economic field data collection manuals and field data collection forms were developed. Later these forms were inserted into Collect surveys with necessary validation rules, codes and restrictions. All these validation rules, automatic calculations, relevance and codes were described in details in the manuals. Prepared manuals and forms are presented in Figure 2.

Figure 2: BFI forms and manuals

The sampling points’ information were listed in a CSV file and imported into Collect; thus, it ensures the exactness of sampling points with their predefined locations (Figure 3).

Figure 3: BFI biophysical sampling point data

According to Field Instructions for the Bangladesh Forest Inventory (BFD 2016) and Field Instructions for the Bangladesh Forest Inventory Socio-economic survey (BFD 2017) all questions were inserted into the Collect schema. This allows for flexible changes for attributes’ properties in the schema when required (Figure 4).

Figure 4: Open Foris Collect schema for survey design in BFI

The number of questions were kept in line with the field manual, field data collection forms and in the Collect survey file. It ensures easy tracking of any question in any format and provides flexibility of cross checking.

Figure 5: Steps towards Open Foris Collect use in BFI

7. Data collection using Open Foris Collect Mobile

Field teams used tablets to collect both biophysical and socio-economic data from the field. The data collection process can be summarized in simple steps mentioned below-

1. Visit plans with date were prepared and submitted using Google Earth images of plot location (plot card)

2. Necessary arrangements (administrative, economic and technical) were made for the visit

3. Field teams visited the pre-determined plot locations (households for socio-economic)

4. Necessary measurements were taken and at the same time data were entered into the Open Foris Collect database

5. Relevant photos were taken using the tablet

6. Error messages were generated by the Open Foris collect those were checked and corrected by the field team. All red dots (errors) and orange dots (warning) were double checked, verified and corrected.

7. In any confusion and clarification field teams were cross checked Collect data management view with the help of the manual (question numbering in labels of Collect schema and manual are consistent)

8. Field manual was used to clarify inconsistencies and confusions observed in the field attributed measurements.

9. A separate official setup arranged to receive the data digitally and address any problems faced by the field teams.

10. A Quality checking team deployed in the field to ensure data quality. The data were checked through a Quality Assurance and Quality Control (QA/QC) process so minimize erroneous data entries.

8. Data management system

Open Foris Collect applies a real time data management process. After completing data collection of any plot or household, the crews sent the data to BFI central unit through Dropbox platform. Data was also sent using other methods, like Google Drive, email or manually submission by pendrive. The BFI unit imported the data into the master Open Foris Collect database. It is good to maintain a separate high configuration computer for data management. After necessary checking according to the data management protocol (Kumar et al. 2017) data were exported from Collect into two formats- .csv and .collect-data files. Using the latest .csv data an access database was maintained. After receiving new data sets, the database was updated. A new database report for each plot was prepared for further checking. For data backup, the exported .collect-data files were archived, where finally these files can be again uploaded into Collect, if needed. Therefore, the BFI data management process is a continuous process (Figure 6).

Figure 6: Data Management system of BFI

9. Data cleansing and quality assurance

The database manager checked the errors and warnings centrally and provided feedback to the field crews. Besides error and warning checks, Open Foris Collect special “data cleansing” module was used. It allows to set a query of checking and it shows whether the results fit with the given condition. This is an automatic way to perform data cleansing quickly.

Even after these multiple checks, some errors may still be present, so data were further checked with R code routines in order to find remaining inconsistencies. If any inconsistency was found, the field team was consulted about the possible reasons and in some cases they were asked to re-collect the information. Finally, a separate QA/QC field team performed checks with field teams present (hot checks) and independently after field teams visited and made measurements (cold checks). Based on this review system if any information was recollected from the field, these data were updated into the Collect database.

Steps of data quality assurance is summarized in Figure 7 and a Data cleansing example is shown in Figure 8-

Figure 7: Data quality assurance steps

Figure 8: Data cleansing using Open Foris Collect in BFI

10. Data visualization and easy reporting

In BFI, several reports were prepared to monitor field work progress and data inconsistencies, including the number of plots completed per team per zone, inconsistency of measured plots identified, and percentage of inaccessible plots. Sometimes the requirements of information/report were really urgent. Open Foris Collect helps to prepare quick calculations and visualize data in the form of graphs, charts, tables etc., without the need to use statistical packages like MS Excel, R/RStudio etc. Open Foris Collect contains an integrated software “Saiku” that can perform these reports easily. In BFI several data visualizations for reporting were prepared by using Saiku.

Customized table preparation using Saiku is an advance function (Figure 9)

Figure 9: Customized table formation using Saiku

Data derived in customized tables can be visualized by bar charts, pie charts, diagrams etc. (Figure 10)

Figure 10: Data presentation using Saiku

Like a GIS application Open Foris Collect can directly show projected plot distribution on the map based on plots’ GPS coordinates. It supports different map types, satellite map, OpenStreet map etc. Open Foris Collect contains the integrated mapping option (Figure 11)

Figure 11: Survey coordinates projection in map using Open Foris Collect

11. Use of open source software in BFI

BFI successfully used different open-source software in different activities from data collection to data analysis. Free software for GIS, land cover assessments and statistical purposes were used along with Open Foris tools.

⮚ Open Foris Collect Mobile: used for field data collection

⮚ Open Foris Collect: Used for data management and reporting

⮚ Microsoft Access: Access database is used to synchronize, storing field data, reporting, and evaluation of data

⮚ QGIS: Used for preparing plot location card and visualize plots in map, cross checking land feature and other objects in the field

⮚ Google Earth: used for cross checking land feature with plot data and other objects in the field

⮚ LCCS: is used to classify the land cover

⮚ RStudio: R with RStudio is used for data quality checking and data analysis

12. Major challenges of using Open Foris Collect in BFI

Open Foris software is continuously under development. Some inconsistencies were found while using Open Foris Collect, which can be improved:

1. Sudden shut down of the application without warning
2. Unexpected data loss due to sudden close of application
3. In few cases Open Foris Collect did not consider errors as per rules and restrictions inserted
4. BFI field team encountered problems in data exporting sometimes
5. In some cases, photos were lost from Open Foris Collect database

In response to these issues faced by the field teams, the BFI team tried to solve the problems. In some cases, support was requested from the Open Foris developer team.

13. Considerations for using Open Foris tools

Open Foris Collect and Collect Mobile are highly effective data collection and management applications. Though some obstacles were found in the BFI program, overall it proved very effective. To use Open Foris tools in a large-scale activity, some important considerations should be kept in mind-

✔ Skilled manpower is required to handle tablet and collect data

✔ Skilled staff is crucial to the design of the survey form in Open Foris Collect

✔ A data management expert is needed to manage the data and deal with the issues identified through the data collection process

✔ It is necessary to establish an effective data management procedure before start field data collection

✔ Necessary manuals and protocols need to be prepared

✔ Data checking criteria need to be finalized earlier and the level of acceptance needs to be fixed.

✔ The required number of tablet, data transfer device (e.g. pendrive), computers for data management are necessary

✔ The data submission modality needs to be finalized before starting field data collection

✔ Good communication should be maintained between the program data manager and Open Foris expert with the Open Foris developer team.

14. Conclusion

Bangladesh Forest Inventory collected 1858 plots Bio-physical data and 6400 household socio-economic data through all over the country using Open Foris Collect. No major inconsistencies were identified throughout the data collection and management process. This approach of BFI data collection by minimizing the use of paper sheets is a good initiative to save natural resources as well as an effective real time data management.

15. References

1. AIMS Team, FAO. (2016). OpenForis: Free Open-Source Solutions for Environmental Monitoring. Link: http://aims.fao.org/activity/blog/openforis-free-open-source-solutions-environmental-monitoring
2. Costello, L., et al. (2016). Experiences in field missions to locate the plots of the 2005 National Forest Assessment of Bangladesh. Dhaka, Bangladesh.
3. BFD. (2016). The Bangladesh Forest Inventory Design. Dhaka, Bangladesh
4. BFD. (2016). Field Instructions for the Bangladesh Forest Inventory. Bangladesh Forest Department and Food and Agricultural Organization of the United Nations. Dhaka, Bangladesh. ISBN: 978–984–34–2709–0
5. BFD. (2017). Field Instructions for the Bangladesh Forest Inventory Socioeconomic survey. Bangladesh Forest Department and Food and Agricultural Organization of the United Nations. Dhaka, Bangladesh. ISBN: 978–984–34–4271–0
6. Kumar, M. F., Costello, L., Mahamud, R., Henry, M., Johnson, K. (2017) Bangladesh Forest Inventory Data Management Protocol. Bangladesh Forest Department and Food and Agricultural Organization of the United Nations. ISBN: 978–984–34–4275–8
7. GOB. (2017). The Socioeconomic survey of Bangladesh. Retrieved from
8. GoB (2019), Tree and forest resources of Bangladesh: Report on the Bangladesh Forest Inventory. Forest Department, Ministry of Environment, Forest and Climate Change, Government of the People’s Republic of Bangladesh, Dhaka, Bangladesh. ISBN: 978–984–34–7255