In recent years, several global land cover change datasets have been derived from various spatial and temporal resolutions of remotely sensed data. Such datasets with accurate and up to date information are necessary for REDD+ land cover change monitoring activities in developing countries most importantly where data gaps are huge. Yet, to complement these datasets to national forest monitoring systems (NFMS) and measuring, reporting and verification (MRV) requirements of REDD+ an assessment of how well locations of mapped change would correspond to actual areas of change on the ground is crucial. Two existing global land cover/use maps derived from different initiatives such as FAO remote sensing survey (RSS) and Hansen et al. global forest cover change (GFCC), with the same remote sensing data sources and spatial resolution were compared to evaluate their accuracy and their adaptability to NFMS. Other external data sources were also explored to see their definition on forest and forest cover change.The study employed two sampling strategies, namely systematic sampling strategy to get access to available RSS tiles and simple random sampling to incorporate Hansen et al. GFCC datasets outside the selected tiles. Our results showed that forest definitions and methodologies for each dataset employed varies, underpinning the variation of forest cover extent and forest loss rate for the same period in Ethiopia. The accuracy of 5 land cover classes from change and no change category were compared and the analysis result indicate that 61% of Hansen et al. GFCC and 80% of FRA RSS datasets showed overall accuracy and agreement with reference data and reasonably acceptable. While, the overall spatial agreement between the two datasets was 53%, which is fairly less than the accuracy observed. With regard to classes stable forest has high agreement with reference data in both datasets with 71% and 83% user accuracy and 62% and 85% producer accuracy in GFCC and RSS, respectively. While other woodland class, is poorly classified in the former case with 25% producer and 51% user accuracy. The reason for more misclassification in Hansen et al. dataset was confusion of land cover classes mainly with agricultural areas and where tree cover is

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.

Covering recent developments in satellite observation data undertaken for monitoring forest areas from global to national levels, this book highlights operational tools and systems for monitoring forest ecosystems. It also tackles the technical issues surrounding the ability to produce accurate and consistent estimates of forest area changes, which are needed to report greenhouse gas emissions and removals from land use changes. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of monitoring methods and shows how state-of-art technologies may soon provide key data for creating more balanced policies.

The Afromontane rainforest of Ethiopia is under the threat of deforestation and forest degradation, despite its significance as one of the only homes for wild populations of coffea arabica. One of the management initiatives was the UNESCO forest Biosphere Reserve (BR) aimed at strengthening policy and regulatory actions among local, regional and global governments and reconciling conservation with local development to monitor forest coverage changes. Assessing the impact of BR forest management and conservation on location, time and intensity of forest disturbances through Man and Biosphere (MaB) programme and REDD+ activities is important for regulatory actions. Empirical evidence on the achievement of this programme was not documented so far. This study is aimed at identifying the roles and assessing spatiotemporal impacts of this BR, together with measuring forest cover change dynamics and the drivers of such change in Yayu Coffee Forest BR of Ethiopia. Using multi-temporal Landsat datasets with in-situ field survey, the study employed BFAST monitor to detect historical forest disturbances and mobile devices to signal changes and collect the relevant data from households. Forest cover change was analysed both spatially (within the BR zones, and between the BR area and the leakage belt), and temporally before and after the implementation of the BR. Qualitative analysis on the role of the BR in tropical areas indicated that progresses in reducing deforestation were observed in areas where MaB aims were appropriately enacted. Landsat image analysis of the study revealed that transition zone has more deforestation rate than both buffer zone and core area within the BR, while relatively high deforestation rate was observed in the leakage belt than in the BR area. After implementation of the BR, average annual deforestation rate was declined from 0.29% to 0.16% in the BR area, and from 0.71% to 0.6% in the leakage belt, respectively, indicating the progress of BR program in reducing deforestation. Farm land expansion, mismanagement of coffee forest, access to forest, road and market were observed as the main drivers and underlying causes of forest cover loss. The findings of the study imply that BR efforts with MaB program and REDD+ activities have enhanced forest cover protections and carbon emission reductions in the study area. Therefore, promoting these initiatives at the local level is important for biodiversity conservation and climate change mitigation strategy of the country.

In this Infobrief, we outline the rules and choices to be addressed by participatory countries in REDD+ activities, and show some technical problems they can face, and some options they can adopt.

This Occasional Paper is a review of the development of Ethiopia’s REDD+ MRV system, its national architecture and policies, progress made so far, and plans for the future. It is not a technical review of the current MRV system. We use published and unpub

REDD+ is one of the leading near-term options for global climate change mitigation. More than 300 subnational REDD+ initiatives have been launched across the tropics, responding to both the call for demonstration activities in the Bali Action Plan and the market for voluntary carbon offset credits.