Recent advancement in spatial data collection technologies dramatically increases the contribution of ordinary people to collect and disseminate geospatial data. At the same time, there is an increasing general agreement that community based forest monitoring can play a crucial role in producing and sharing information about the condition of forest resources in time and space. Despite the advantages of the community based monitoring, there are also doubts and concerns that existed in the scientific community related to the quality of the data. Therefore, this research is aiming to assess the quality of forest monitoring activity data sets, which is collected by local experts in Kafa Biosphere Reserve in Ethiopia. The research was conducted to test the quality of local experts data for REDD+ mechanism to track the forest change and carbon emissions. In this research, we examines the quality of local experts data relative to the reference data sets of remotes sensing time series images of 2005 to 2012, GIS data sets, and ground based validation measurements. The main variables are date of forest disturbances, size of the forest disturbance, drivers information, location and coverage of forest disturbances. The spatial variables of the local experts data were assessed using the spatial data quality parameters whereas the temporal variables were compared through BFAST monitoring on Landsat time series images and visual interpretations on high resolution images of Spot and Rapid Eye. The results show that the local experts can perform and produce quality data comparable to validation measurements by experts. We found a regression coefficient value of 0.84 for area/size estimation and ~65% of correctly classification accuracy of drivers information of forest disturbances. Furthermore, the result confirms that local experts have a short time delay in detecting forest disturbances compared to high resolution remote sensing time series data of Spot 5-Rapid Eye satellite images than of Landsat imagery. Based up on the findings of this study, we suggests that the local expert data can enhance the quality of forest monitoring data of remote sensing particularly in detecting near real time forest disturbances.

In long-term and large-scale environmental and forest monitoring schemes, a large amount of data is collected over long periods, which needs to be managed in a cost-efficient, transparent, and sustainable database management system (DBMS). Centralized data collection and validation by a coordination unit responsible for the establishment of rules for data transfer and checking routines is recommended. The ultimate objective being pursued by all partners active in a large monitoring program is to develop a state-of-the-art, Web-based information storage, and delivery system capable of ensuring reliable and user friendly service for data providers and data users as well as for administrative work by the coordination unit. General principles and procedures for data preparation, data quality control, and storage using a DBMS are presented. A comprehensive approach offers tools for dissemination of data, metadata, and results, as well as an automatic reporting application integrated with a WebGIS. Access rights for users are controlled by a rights-and-role management tool. As an example, the ICP Forests database system is described.

Vegetation-related response variables adopted in the ICP Forests are related to health, growth, phenology, and diversity. Their assessment and measurement is subject to errors, which need to be controlled and documented. To do this, data quality requirements (DQRs) and intercomparison exercises were implemented. During 2009 and 2010, 111–260 field crews took part in different exercises organized across Europe. Results revealed that, while for some variables (e.g., tree diameter, standing basal area, ozone injury, species diversity) DQRs were substantially achieved, problems still exist for other measurements/calculations (tree height, volume and increment, crown base height, crown symptoms identification and description). In some cases, achievement of DRQs was partly due to relaxed DQRs. While the recent progresses in Quality Assurance/Quality Control for field surveys are promising, further effort is necessary to sharpen DQRs, refine standard operating procedures, and reinforce training.

Chemical analyses are an essential part of forest ecosystem monitoring activities. Harmonized, well-defined, and documented analytical methods are an important part of a laboratory Quality Control program and are essential to maximize spatial and temporal comparability of monitoring data. Different quality and data consistency checks, plausible ranges for analytical results, use of reference materials, and control charts are described for internal laboratory Quality Control. On the other side, coordinated ring tests among participating laboratories are an integral part of external Quality Control. Organization of ring tests, evaluation, and the benchmark with tolerable limits are described and specified. Results for water, foliage, and soil ring tests and the evaluation of quality indicators within the European forest ecosystem monitoring program are reported.

The international, multipartner, multiagency nature of the ICP Forests conditioned its design from the beginning, with a number of trade-offs necessary to accommodate specific needs, perspectives, and monitoring “traditions.” Reconciling these differences with the program objectives is a major challenge for program managers and stakeholders. In this chapter, basic design concepts and principles of the ICP Forests are described, together with more recent efforts undertaken to respond to that major challenge. Emphasis is placed on fixed-area plot design for both Levels I and II monitoring intensity, as well as the need to maintain data time series. The newly developed Quality Assurance perspective, the enhanced data management system, and the policy for intellectual property, publication, and data distribution are also important elements of the entire monitoring design.

To be useful in a forest and environmental management perspective, monitoring data need to provide conclusive results in terms of forest's status, changes, and their correlates. To be fit for such a use, monitoring data must satisfy a number of requirements. In particular, well-defined objectives, sound statistical design, and known and documented metrological quality are essential for defensible data, results, and information. Together with other sources of error (nonstatistical errors, model errors, definition errors), they need to be considered at the design stage of the monitoring. The value of a comprehensive Quality Assurance (QA) perspective is emphasized in that it forces program managers and designers to consider and provide explicit answers and solutions for each individual step of the monitoring program.