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Reference data for large-scale land cover map are commonly acquired by visual interpretation of remotely sensed data. To assure consistency, multiple images are used, interpreters are trained, sites are interpreted by several individuals, or the procedure includes a review. But little is known about important factors influencing the quality of visually interpreted data. We assessed the effect of multiple variables on land cover class agreement between interpreters and reviewers. Our analyses concerned data collected for validation of a global land cover map within the Copernicus Global Land Service project. Four cycles of visual interpretation were conducted, each was followed by review and feedback. Each interpreted site element was labelled according to dominant land cover type. We assessed relationships between the number of interpretation updates following feedback and the variables grouped in personal, training, and environmental categories. Variable importance was assessed using random forest regression. Personal variable interpreter identifier and training variable timestamp were found the strongest predictors of update counts, while the environmental variables complexity and image availability had least impact. Feedback loops reduced updating and hence improved consistency of the interpretations. Implementing feedback loops into the visually interpreted data collection increases the consistency of acquired land cover reference data. [ABSTRACT FROM AUTHOR]

Rain gauge networks are crucial for enhancing the spatio‐temporal characterization of precipitation. In tropical regions, scarcity of rain gauge data, climatic variability, and variable spatial accessibility make conventional approaches to design rain gauge networks inadequate and impractical. In this study, we propose the use of conditioned Latin hypercube sampling (cLHS) method with multi‐temporal layers of remotely sensed precipitation measurements for capturing the spatio‐temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon region of Ecuador, for which monthly precipitation averages were derived based on a 16‐year period of Tropical Rainfall Measuring Mission (TRMM 3B43 V7) data which were used as prior information to select representative sampling points through cLHS. Two scenarios for the sampling design were considered and evaluated, one without and one with restrictions on accessible sites according to the proximity to roads and settlements. Results showed that both optimized networks captured the variability of precipitation according to the TRMM climatology. Furthermore, evaluation against an independent satellite precipitation data set showed that the optimized networks support mapping precipitation based on ordinary kriging (OK). Comparison with regular and random sampling methods showed that particularly when a practical scenario is considered, the optimized network provided more reliable results over time, highlighting the suitability of the network to capture temporal changes and map precipitation with high accuracy. The proposed approach could be easily adopted in other ungauged and poorly accessible regions for rain gauge network design as well as to the design of multi‐objective monitoring networks. A sampling scheme to monitor precipitation distribution along the year is proposed thought the use of conditioned Latin hypercube sampling and satellite precipitation images. It was applied on monthly TRMM images in the relatively ungauged region of the Ecuadorian Amazon, in which accessibility restrictions were considered. The proposed method captured effectively the spatial and temporal changes of precipitation and supports mapping precipitation in the region. [ABSTRACT FROM AUTHOR]

Global scale land cover (LC) mapping has interested many researchers over the last two decades as it is an input data source for various applications. Current global land cover (GLC) maps often do not meet the accuracy and thematic requirements of specific users. This study aimed to create an improved GLC map by integrating available GLC maps and reference datasets. We also address the thematic requirements of multiple users by demonstrating a concept of producing GLC maps with user-specific legends. We used a regression kriging method to integrate Globcover-2009, LC-CCI-2010, MODIS-2010 and Globeland30 maps and several publicly available GLC reference datasets. Overall correspondence of the integrated GLC map with reference LC was 80% based on 10-fold cross-validation using 24,681 sample sites. This is globally 10% and regionally 6–13% higher than the input map correspondences. Based on LC class presence probability maps, expected LC proportion maps at coarser resolution were created and used for characterizing mosaic classes for land system modelling and biodiversity assessments. Since more reference datasets are becoming freely accessible, GLC mapping can be further improved by using the pool of all available reference datasets. LC proportion information allow tuning LC products to specific user needs. [ABSTRACT FROM PUBLISHER]

According to the latest report of the Intergovernmental Panel on Climate Change (IPCC), emissions must be cut by 41-72 % below 2010 levels by 2050 for a likely chance of containing the global mean temperature increase to 2°C. The AFOLU sector (Agriculture, Forestry and Other Land Use) roughly contributes with a quarter (~ 10-12 PgCO2e.yr-1) of the net anthropogenic GHG emissions mainly from deforestation, fire, wood harvesting, and agricultural emissions including croplands, paddy rice and livestock. In spite of the importance of this sector, it is unclear where are the regions in the planet with AFOLU emissions hotspots, and how uncertain these emissions are. Here we present a novel spatially comparable dataset containing annual mean estimates of gross AFOLU emissions (CO2, CH4, N2O), associated uncertainties, and leading emission sources, in a spatially disaggregated manner (0.5°), for the tropics, for the period 2000-2005. Our data highlight: i) the existence of AFOLU emissions hotspots on all continents, with particular importance of evergreen rainforest deforestation in Central and South America, fire in dry forests in Africa, and both peatland emissions and agriculture in Asia; ii) a predominant contribution of forests and CO2 to the total AFOLU emissions (75 %) and to their uncertainties (98 %), iii) higher gross fluxes from forests coincide with higher uncertainties, making agricultural hotspots more appealing for effective mitigation action, and iv) a lower contribution of non-CO2 agricultural emissions to the total gross budget (ca. 25 %) with livestock (15.5 %) and rice (7 %) leading the emissions. Gross AFOLU tropical emissions 8.2 (5.5-12.2) were in the range of other databases 8.4 and 8.0 PgCO2e.yr-1 (FAOSTAT and EDGAR respectively), but we offer a spatially detailed benchmark for monitoring progress on reducing emissions from the land sector in the tropics. The location of the AFOLU hotspots of emissions and data on their associated uncertainties, will assist national policy makers, investors and other decision-makers who seek to understand the mitigation potential of the AFOLU sector. [ABSTRACT FROM AUTHOR]

Monitoring tropical deforestation and forest degradation is one of the central elements for the Reduced Emissions from Deforestation and Forest Degradation in developing countries (REDD+) scheme. Current arrangements for monitoring are based on remote sensing and field measurements. Since monitoring is the periodic process of assessing forest stands properties with respect to reference data, adopting the current REDD+ requirements for implementing monitoring at national levels is a challenging task. Recently, the advancement in Information and Communications Technologies (ICT) and mobile devices has enabled local communities to monitor their forest in a basic resource setting such as no or slow internet connection link, limited power supply, etc. Despite the potential, the use of mobile device system for community based monitoring (CBM) is still exceptional and faces implementation challenges. This paper presents an integrated data collection system based on mobile devices that streamlines the community-based forest monitoring data collection, transmission and visualization process. This paper also assesses the accuracy and reliability of CBM data and proposes a way to fit them into national REDD+ Monitoring, Reporting and Verification (MRV) scheme. The system performance is evaluated at Tra Bui commune, Quang Nam province, Central Vietnam, where forest carbon and change activities were tracked. The results show that the local community is able to provide data with accuracy comparable to expert measurements (index of agreement greater than 0.88), but against lower costs. Furthermore, the results confirm that communities are more effective to monitor small scale forest degradation due to subsistence fuel wood collection and selective logging, than high resolution remote sensing SPOT imagery. [ABSTRACT FROM AUTHOR]

This paper proposes and illustrates a decision analytical approach to compare the value of alternative spatial data sets. In contrast to other work addressing value of information, its focus is on value of control. This is a useful concept when choosing the best data set for decision making under uncertainty due to error in the reported data. Application of the concept requires probabilistic accuracy measures and a loss function representing the cost of incorrect judgement about some target property. This is illustrated by an assessment of the suitability of two digital elevation models (DEMs) for determining the volume of sand required for building a container port. To demonstrate flexibility of the approach, accuracy assessment was based on both a random and a systematic sample of error data, using design-based estimation and model-based prediction, that is geostatistics. Analysis results included the expected loss for each combination of DEM and sampling strategy. These indicated that both DEMs were equally suitable for the intended use. Operational practicability of the method is highly dependent on the willingness of database producers to give access to sample information similar to the quick looks provided to potential users of remote sensing imagery. [ABSTRACT FROM AUTHOR]