The agroforestry parklands in the Sudano-Sahelian zone are of critical importance for food security, but face several challenges in terms of changes in climate and land use. The ability to systematically monitor crop productivity in these systems is therefore of importance for both informing land management policies and studying long-term trends. This study, conducted in two different agroecological areas in southern and central Burkina Faso covering two climate-wise very contrasting years (2020–2021), is an initial step to designing a system based on satellite remote sensing that enables national-scale monitoring of crop productivity. In these two sites, we collected large field datasets of crop productivity (150 plots) for use in model training and validation. The main assessments focused on how to best process and combine remote sensing data sources, including time series from the Sentinel-1 and Sentinel-2 satellite systems, as well as soil properties, elevation and tree cover. Other key focuses were evaluating different regression modelling algorithms (multilinear and machine learning) and clarifying the potential benefits of performing the modelling in specific geographic regions and years or if the modelling can be generalized. Overall, the results show that accurate estimates of crop productivity are achievable using the proposed modelling framework, with encouragingly high R2 (0.49–0.82) and low root mean square errors (11.80–19.35%). Sentinel-2 was the most important data source, but our results also demonstrate the potential of Sentinel-1, which has the benefit of not being affected by clouds. Another encouraging aspect is that the results were stable both between the years, which differed significantly in terms of rainfall and crop productivity, and between the sites that are characterized by contrasting crop compositions. This study shows that the development of a national-level crop monitoring system in Burkina Faso or countries with similar environmental conditions is within reach.

Trees in agroforestry parklands influence crops both through competitive and facilitative mechanism, but the effects are challenging to disentangle due to the complexity of the system with high variability in tree cover structure and species diversity and crop combinations. Focusing on a landscape in central Burkina Faso domi- nated by Vitellaria paradoxa and Parkia biglobosa, this paper examines how tree cover influences crop yield at landscape scale using satellite data and spatial statistics. Our analysis is based on data from 2017 to 2018 with differences in rainfall to assess the stability in identified relationships. Our findings showed that tree canopy cover and tree density inside the fields tended to decrease crop yield because of competition, but also that these variables when considering the surrounding landscape exerted an opposite effect because of their buffering ef- fects. The explanatory variables representing soil properties did have limited effects on crop yield in this study. These patterns were consistent during the two years of monitoring. Overall, our results suggest that farmers in this area might manage the tree cover in a way that optimizes sustainable yields as canopy cover and tree density in most parklands is below the limits identified here where competition outweight the facilitative effects. 

Mapping wetland types in northern-latitude regions with Earth Observation (EO) data is important for several practical and scientific applications, but at the same time challenging due to the variability and dynamic nature in wetland features introduced by differences in geophysical conditions. The objective of this study was to better understand the ability of Sentinel-1 radar data, Sentinel-2 optical data and terrain derivatives derived from Copernicus digital elevation model to distinguish three main peatland types, two upland classes, and surface water, in five contrasting landscapes located in the northern parts of Alaska, Canada and Scandinavia. The study also investigated the potential benefits for classification accuracy of using regional classification models constructed from region-specific training data compared to a global classification model based on pooled reference data from all five sites. Overall, the results show high promise for classifying peatland types and the three other land cover classes using the fusion approach that combined all three EO data sources (Sentinel-1, Sentinel-2 and terrain derivatives). Overall accuracy for the individual sites ranged between 79.7% and 90.3%. Class specific accuracies for the peatland types were also high overall but differed between the five sites as well as between the three classes bog, fen and swamp. A key finding is that regional classification models consistently outperformed the global classification model by producing significantly higher classification accuracies for all five sites. This suggests for progress in identifying effective approaches for continental scale peatland mapping to improve scaling of for example, hydrological- and greenhouse gas-related processes in Earth system models.

The need to develop sustainable stormwater management is intensifying due to climate impacts and urban densification. Such complex planning processes require insights into disparate issues, connecting heterogeneous actors. While many decision-support tools are developed to facilitate such planning, research assessing their usefulness is requested. This study introduces and assesses one such ICT-tool; the Visual Water platform, aiming to support sustainable stormwater planning in Swedish municipalities. The study aims to identify critical points to consider for developers of related decision-support tools and to detangle requirements and tradeoffs in making them relevant and user-friendly, building on test-sessions with Swedish practitioners. Results show that the platform responds to challenges within municipal planning as outlined by Swedish practitioners. However, though the platform content is considered relevant, its application in real-world planning is perceived as somewhat unclear. The paper discusses ideas for how sustainability-related decision-support tools better can respond to user demands.

Appropriate methods to measure greenhouse gas (GHG) fluxes are critical for our ability to detect fluxes, understand regulation, make adequate priorities for climate change mitigation efforts, and verify that these efforts are effective. Ideally, we need reliable, accessible, and affordable measurements at relevant scales. We surveyed present GHG flux measurement methods, identified from an analysis of >11000 scientific publications and a questionnaire to sector professionals and analysed method pros and cons versus needs for novel methodology. While existing methods are well-suited for addressing certain questions, this presentation presents fundamental limitations relative to GHG flux measurement needs for verifiable and transparent action to mitigate many types of emissions. Cost and non-academic accessibility are key aspects, along with fundamental measurement performance. These method limitations contribute to the difficulties in verifying GHG mitigation efforts for transparency and accountability under the Paris agreement. Resolving this mismatch between method capacity and societal needs is urgently needed for effective climate mitigation. This type of methodological mismatch is common but seems to get high priority in other knowledge domains. The obvious need to prioritize development of accurate diagnosis methods for effective treatments in healthcare is one example. This presentation provides guidance regarding the need to prioritize the development of novel GHG flux measurement methods.

Dataset used in the publication "Multi-source mapping of peatland types using Sentinel-1, Sentinel-2 and terrain derivatives – A comparison between five high-latitude landscapes". The dataset includes preprocessed predictor variables in image format (geoTIFF) from Sentinel-1, Sentinel-2 and Copernicus DEM for the five sites, including North Slope (Alaska), Yukon (Canada), Great Slave Lake (Canada), Hudson Bay Lowlands (Canada) and northern Sweden (Scandinavia). It also includes reference data (shape files) used for training and validation of classification models.

Visual Water (http//visualwater.se) är en interaktiv webbaserad visualiseringsplattform som syftar till att stötta svenska kommuner i arbetet för en hållbar dagvatten- och skyfallshantering. Plattformen är utformad för att svara mot centrala utmaningar som lyfts av svenska dagvattenaktörer som befinner sig i skiftet bort från de rörbundna nätverksidealen för avledning av dagvatten och strävar efter en högre grad av grön-blå och öppna lösningar i stadsmiljön.

Visual Water (http//visualwater.se) is an interactive web-based platform for geographic and information visualization aiming to support Swedish municipalities working towards sustainable stormwater management. The content and functionalities of the platform are designed to respond to central challenges as they are defined by actors in the Swedish stormwater sector who find themselves in the shift away from underground pipe-bound solutions towards blue-green measures in the urban environment.

Cyclones are one of the most common and foremost natural hazards in the world that causes extensive causalities. Bangladesh is highly vulnerable to cyclone hazard for its geographical location and socio-economic conditions. This study has aimed to analyze the historical cyclonic hazards and creating vulnerability maps and risk maps for Bangladesh. The apposite variables were selected by reviewing pertinent literatures and necessary data were retrieved for 1900 to 2015. GIS tool has been used for visualization of weighed scores for hazard, vulnerability and risk based on historical cyclones’ intensities, magnitudes, causalities and existing coping capacities. Moreover, hotspot analysis that implies Getis-Ord Gi* spatial statistics was also used in this study to identify the patterns of spatial significance and relationship of areas among their neighbors. This analysis produced Z scores from weighed variables those were proportional to the degree of vulnerability and risk. The low negative to high positive Z scores are correlative of low to high cyclone vulnerability and risk. Consequently, the weighed scores have elicited the coastal areas are in front line in terms of vulnerability and risk to cyclone. Besides, Gi* revealed that some areas are significantly risk prone for being spatially influenced by their neighbors.

High resolution satellite systems enable efficient and detailed mapping of tree cover, with high potential to support both natural resource monitoring and ecological research. This study investigates the capability of multi-seasonal WorldView-2 imagery to map five dominant tree species at the individual tree crown level in a parkland landscape in central Burkina Faso. The Random Forest algorithm is used for object based tree species classification and for assessing the relative importance of WorldView-2 predictors. The classification accuracies from using wet season, dry season and multi-seasonal datasets are compared to gain insights about the optimal timing for image acquisition. The multi-seasonal dataset produced the most accurate classifications, with an overall accuracy (OA) of 83.4%. For classifications based on single date imagery, the dry season (OA=78.4%) proved to be more suitable than the wet season (OA=68.1%). The predictors that contributed most to the classification success were based on the red edge band and visible wavelengths, in particular green and yellow. It was therefore conchided that WorldView-2, with its unique band configuration, represents a suitable data source for tree species mapping in West African parklands. These results are particularly promising when considering the recently launched WorldView-3, which provides data both at higher spatial and spectral resolution, including shortwave infrared bands. (C) 2016 Elsevier B.V. All rights reserved.