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.

Methane (CH4), one of the key long-lived atmospheric greenhouse gases, is primarily produced from organic matter. Accordingly, net primary production of organic matter sets the boundaries for CH4 emissions. Plants, being dominant primary producers, are thereby indirectly sustaining most global CH4 emissions, albeit with delays in time and with spatial offsets between plant primary production and subsequent CH4 emission. In addition, plant communities can enhance or hamper ecosystem production, oxidation, and transport of CH4 in multiple ways, e.g., by shaping carbon, nutrient, and redox gradients, and by representing a physical link be-tween zones with extensive CH4 production in anoxic sediments or soils and the atmosphere. This review focuses on how plants and other primary producers influence CH4 emissions with the consequences at ecosystem scales. We outline mechanisms of interactions and discuss flux regulation, quantification, and knowledge gaps across multiple ecosystem examples. Some recently proposed plant-related ecosystem CH4 fluxes are difficult to reconcile with the global atmospheric CH4 budget and the enigmas related to these fluxes are highlighted. Overall, ecosystem CH4 emissions are strongly linked to primary producer communities, directly or indirectly, and properly quantifying magnitudes and regulation of these links are key to predicting future CH4 emissions in a rapidly changing world.

Reaching climate goals depends on appropriate and accurate methods to quantify greenhouse gas (GHG) fluxes and to verify that efforts to mitigate GHG emissions are effective. We here highlight critical advantages, limitations, and needs regarding GHG flux measurement methods, identified from an analysis of >13 500 scientific publications regarding three long-lived GHGs, carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). While existing methods are well-suited for assessing atmospheric changes and local fluxes, they are expensive and have limited accessibility. Further, we are typically forced to choose between methods for very local GHG sources and sinks and their regulation (m(2)-scaled measurements), or methods for aggregated net fluxes at >ha or km(2) scales measurements. The results highlight the key need of accessible and affordable GHG flux measurement methods for the many flux types not quantifiable from fossil fuel use, to better verify inventories and mitigation efforts for transparency and accountability under the Paris agreement. The situation also calls for novel methods, capable of quantifying large scale GHG flux patterns while simultaneously distinguishing local source and sink dynamics and reveal flux regulation, representing key knowledge for quantitative GHG flux modeling. Possible strategies to address the identified GHG flux measurement method needs are discussed. The analysis also generated indications of how GHG flux measurements have been distributed geographically and across flux types, which are reported.

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.

Many biochemical processes and dynamics are strongly controlled by terrain topography, making digital elevation models (DEM) a fundamental dataset for a range of applications. This study investigates the quality of four pan-Arctic DEMs (Arctic DEM, ASTER DEM, ALOS DEM and Copernicus DEM) within the Kalix River watershed in northern Sweden, with the aim of informing users about the quality when comparing these DEMs. The quality assessment focuses on both the vertical accuracy of the DEMs and their abilities to model two fundamental elevation derivatives, including topographic wetness index (TWI) and landform classification. Our results show that the vertical accuracy is relatively high for Arctic DEM, ALOS and Copernicus and in our study area was slightly better than those reported in official validation results. Vertical errors are mainly caused by tree cover characteristics and terrain slope. On the other hand, the high vertical accuracy does not translate directly into high quality elevation derivatives, such as TWI and landform classes, as shown by the large errors in TWI and landform classification for all four candidate DEMs. Copernicus produced elevation derivatives with results most similar to those from the reference DEM, but the errors are still relatively high, with large underestimation of TWI in land cover classes with a high likelihood of being wet. Overall, the Copernicus DEM produced the most accurate elevation derivatives, followed by slightly lower accuracies from Arctic DEM and ALOS, and the least accurate being ASTER.

Mapping of tree height is of great importance for management, planning, and research related to agroforestry parklands in Africa. In this paper, we investigate the potential of spotlight-mode data from the interferometric synthetic aperture radar (InSAR) satellite system TanDEM-X (TDM) for mapping of tree height in Sapone, Burkina Faso, a test site characterised by a low average canopy cover (similar to 15%) and a mean tree height of 9.0 m. Seven TDM acquisitions from January-April 2018 are used jointly to create high-resolution (similar to 3 m) maps of interferometric phase height and mean canopy elevation, the latter derived using a new, model-based processing approach compensating for some effects of the side-looking geometry of SAR. Compared with phase height, mean canopy elevation provides a more accurate representation of tree height variations, a better tree positioning accuracy, and better tree height estimation performance when assessed using 915 trees inventoried in situ and representing 15 different species/genera. We observe and discuss two bias effects, and we use empirical models to compensate for these effects. The best-performing model using only TDM data provides tree height estimates with a standard error (SE) of 2.8 m (31% of the average height) and a correlation coefficient of 75%. The estimation performance is further improved when TDM height data are combined with in situ measurements; this is a promising result in view of future synergies with other remote sensing techniques or ground measurement-supported monitoring of well-known trees.

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.

Climate change impacts, ageing infrastructure and the increasing imperviousness of cities all raise enormous challenges to and call for new ways of planning for sustainable urban stormwater management. Especially, closer collaboration among a diverse set of actors involved has been pointed to as critical to enable the development of holistic and flexible approaches. However, the shift towards inclusive forms of planning has been slow, and characterized by technical and institutional lock-ins. Against this background, this study scrutinizes the challenges and developments perceived as central for improving stormwater planning, and analyzes how formal and informal institutional change could contribute to enhancing sustainability in this sector. Building on an analysis of data from workshops, interviews and a survey with Swedish planners and water managers, we suggest new strategies for integrating stormwater concerns into planning processes, overcoming silo structures, fostering cocreation cultures, and securing the continuation and implementation of stormwater management through various planning stages.

Crop production statistics at the field scale are scarce in African countries, limiting potential research on yield gaps as well as monitoring related to food security. This paper examines the potential of using Sentinel-2 time series data to derive spatially explicit estimates of crop production in an agroforestry parkland in central Burkina Faso. This type of landscape is characterized by agricultural fields where cereals (millet and sorghum) and legumes (cowpea) are intercropped under a relatively dense tree canopy. We measured total above ground biomass (AGB) and grain yield in 22 field plots at the end of two growing seasons (2017 and 2018) that differed in rainfall timing and amount. Linear regression models were developed using the in situ crop production estimates and temporal metrics derived from Sentinel-2 time series. We studied several important aspects of satellite-based crop production estimation, including (i) choice of vegetation indices, (ii) effectiveness of different time periods for image acquisition and temporal metrics, (iii) consistency of the method between years, and (iv) influence of intercropping and trees on accuracy of the estimates. Our results show that Sentinel-2 data were able to explain between 41 and 80% of the variation in the in situ crop production measurements, with relative root mean square error for AGB estimates ranging between 31 and 63% in 2017 and 2018, respectively, depending on temporal metric used as estimator. Neither intercropping of cereals and legumes nor tree canopy cover appeared to influence the relationship between the satellite-derived estimators and crop production. However, inter-annual rainfall variations in 2017 and 2018 resulted in different ratios of AGB to grain yield, and additionally, the most effective temporal metric for estimating crop production differed between years. Overall, this study demonstrates that Sentinel-2 data can be an important resource for upscaling field measurements of crop production in this agroforestry system in Burkina Faso. The results may be applicable in other areas with similar agricultural systems and increase the availability of crop production statistics.

Citizen participation is obligated in municipal planning but is often criticized because of its inadequate implementation. To increase the usefulness of citizen participation and the involvement of underrepresented groups, online participatory tools (OPTs) have attracted attention, in particular, on topics related to climate change. Although many OPTs have been developed and are becoming more widely used, assessments of their usefulness in real-world planning remain scarce. This study aimed to disentangle prospects and pitfalls of this still novel way of practicing citizen dialogue. Specifically, we apply criteria derived from related literatures to assess a mainstreamed OPT in Norrkoping, Sweden. The CityPlanner (TM) tool was discussed with citizens and planners using focus group methodology and semi-structured individual interviews. Moreover, citizen contributions in four applications of the OPT were analyzed. The results reveal that the biggest challenges for citizen dialogues on planning in general and on climate change, in particular, appear not mainly rooted in the technical functions of the OPT. Rather, problems lie in (i) the lack of municipal strategy for citizen participation and in applying OPTs, (ii) a disparagement in citizens abilities to contribute to forming robust and sustainable cities, and (iii) in diverging views about the role of citizen contributions. This is reflected in how the OPT is used. While the examined OPT shows potential, the results indicate that visualized contexts for planning might be too scant to be entirely meaningful, and it lacks mechanisms for feedback. Not using the full potential of the OPT makes citizens less engaged and risks to adversely affect learning and citizens contributions to solving complex issues.

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.

The spatial extent of northern peatlands remains highly uncertain in spite of rapidly developing satellite observation datasets. This is limiting progress in the understanding of fundamental biogeochemical processes, such as the global carbon (C) cycle and climate feedback effects on C fluxes. In this study, we evaluated the capabilities of two new satellite datasets that enable regional scale mapping of peatland extent at high spatial resolution, including Sentinel-1 synthetic aperture radar (SAR) and the Arctic digital elevation model (ArcticDEM). Terrain indices and temporal features derived from these datasets provided input to Random Forest models for delineating four main land cover classes (forest, open upland, water and peatland) in an area in northern Sweden consisting of both lowland and mountainous terrain. The contribution of ArcticDEM to the classification accuracy was assessed by comparing the results with those derived when a high quality LiDAR based DEM (LiDEM) was used as alternative model input. This study shows that multi-seasonal SAR alone can produce reasonable classification results in terms of overall accuracy (OA; 81.6%), but also that it has limitations. The inclusion of terrain indices improved classification performance substantially. OA increased to 87.5% and 90.9% when terrain indices derived from ArcticDEM and LiDEM were included, respectively. The largest increase in accuracy was achieved for the peatland class, which suggests that terrain indices do have the ability to capture the features in the geographic context that aid the discrimination of peatland from other land cover classes. The relatively small difference in classification accuracy between LiDEM and ArcticDEM is encouraging since the latter provides circumpolar coverage. Thus, the combination of Sentinel-1 time series and terrain indices derived from ArcticDEM presents opportunities for substantially improving regional estimates of peatland extent at high latitudes.

We investigated the correlation between backscatter and soil moisture considering precipitation and crop effects using dual polarized Sentinel-1A data. The analyzed data consist of a time-series of 38 Sentinel-1A GRD images acquired on a 12-days repeat cycle from July 2017 to October 2018 over Sapone in Burkina Faso. We show that the temporal change of backscatter corresponds to the soil moisture content rather than crops.

The calibration and validation of remote sensing land cover products are highly dependent on accurate field reference data, which are costly and practically challenging to collect. We describe an optical method for collection of field reference data that is a fast, cost-efficient, and robust alternative to field surveys and UAV imaging. A lightweight, waterproof, remote-controlled RGB camera (GoPro HERO4 Silver, GoPro Inc.) was used to take wide-angle images from 3.1 to 4.5 m in altitude using an extendable monopod, as well as representative near-ground (amp;lt; 1 m) images to identify spectral and structural features that correspond to various land covers in present lighting conditions. A semi-automatic classification was made based on six surface types (graminoids, water, shrubs, dry moss, wet moss, and rock). The method enables collection of detailed field reference data, which is critical in many remote sensing applications, such as satellite-based wetland mapping. The method uses common non-expensive equipment, does not require special skills or training, and is facilitated by a step-by-step manual that is included in the Supplement. Over time a global ground cover database can be built that can be used as reference data for studies of non-forested wetlands from satellites such as Sentinel 1 and 2 (10 m pixel size).

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.

Scarcity of in situ vegetation data inhibits research and natural resource management in the Sudano- Sahelian zone (SSZ). Satellite and aerial remote sensing (RS) constitute key technologies for improving the availability of vegetation data, and consequently the preconditions for scientific analysis and monitoring. The aim of this paper was to investigate how the hands-on application of RS for vegetation analysis has developed in the SSZ by reviewing the scientific literature published between 1975 and 2014. The paper assesses the usages and the users of RS by focusing on four aspects of the material (268 peer-reviewed articles), including publication details (time of publication, scientific discipline of journals and author nationality), geographic information (location of study areas and spatial scale of research), data usage (application of RS systems and procedures for accuracy assessments), and research topic (scientific objective of the research). Three key results were obtained: i) the application of RS to analyze vegetation in the SSZ has increased consistently since 1977 and it seems to become adopted by a growing number of scientific disciplines; ii) the contribution of African authors is low, potentially signalling a need for an increased transfer of knowledge and technology from developed countries; iii) RS has pri- marily been used to analyze changes in vegetation productivity and broad vegetation types, whereas its use for studying interactions between vegetation and environmental factors has been relatively low. This calls for stronger collaborative RS research that enables the mapping of additional vegetation variables of high relevance for the environmental problems facing the SSZ. Remotely sensed vegetation data are needed at spatial scales that suits the requirements of both research and natural resource management in order to further enhance the usefulness of this technology. 

How much of India’s vast wasteland can be used for growing plants such as eucalyptus and Jatropha? As land demands have increased, the sustainable use of marginal lands has become increasingly important. In India about 47 million hectares, or 15 percent of the total geographical area, is classified as wastelands. Here we assess the climate and land quality requirements of eucalyptus, a commonly used plantation tree, and Jatropha, a much-discussed biodiesel crop. We find that roughly half of the degraded lands are suitable for growing eucalyptus and/or Jatropha. 

Accurate and timely maps of tree cover attributes are important tools for environmental research and natural resource management. We evaluate the utility of Landsat 8 for mapping tree canopy cover (TCC) and aboveground biomass (AGB) in a woodland landscape in Burkina Faso. Field data and WorldView-2 imagery were used to assemble the reference dataset. Spectral, texture, and phenology predictor variables were extracted from Landsat 8 imagery and used as input to Random Forest (RF) models. RF models based on multi-temporal and single date imagery were compared to determine the influence of phenology predictor variables. The effect of reducing the number of predictor variables on the RF predictions was also investigated. The model error was assessed using 10-fold cross 

validation. The most accurate models were created using multi-temporal imagery and variable selection, for both TCC (five predictor variables) and AGB (four predictor variables). The coefficient of determination of predicted versus observed values was 0.77 for TCC (RMSE = 8.9%) and 0.57 for AGB (RMSE = 17.6 tons∙ha−1). This mapping approach is based on freely available Landsat 8 data and relatively simple analytical methods, and is therefore applicable in woodland areas where sufficient reference data are available. 

Woodlands constitute the subsistence base of the majority of people in the Sudano-Sahelian zone (SSZ), but low availability of in situ data on vegetation structure and composition hampers research and monitoring. This thesis explores the utility of remote sensing for mapping and analysing vegetation, primarily trees, in the SSZ. A comprehensive literature review was first conducted to describe how the application of remote sensing has developed in the SSZ between 1975 and 2014, and to identify important research gaps. Based on the gaps identified in the literature review, the capabilities of two new satellite systems (WorldView-2 and Landsat 8) for mapping woodland structure and composition were tested in an area in central Burkina Faso.

The results shows that WorldView-2 represents a useful data source for mapping individual trees: 85.4% of the reference trees were detected in the WorldView-2 data and tree crown area was estimate with an average error of 45.6%. In addition, WorldView-2 data produced high classification accuracies for five locally important tree species. The highest overall classification accuracy (82.4%) was produced using multi-temporal WorldView-2 data. Landsat 8 data proved more suitable for mapping tree canopy cover as compared to aboveground biomass in the woodland landscape. Tree canopy cover and aboveground biomass was predicted with 41% and 66% root mean square error, respectively, at pixel level.

This thesis demonstrates the potential of easily accessible data from two satellite systems for mapping important tree attributes in woodland areas, and discusses how the usefulness of remote sensing for analyzing vegetation can be further enhanced in the SSZ.

Detailed information on tree cover structure is critical for research and monitoring programs targeting African woodlands, including agroforestry parklands. High spatial resolution satellite imagery represents a potentially effective alternative to field-based surveys, but requires the development of accurate methods to automate information extraction. This study presents a method for tree crown mapping based on Geographic Object Based Image Analysis (GEOBIA) that use spectral and geometric information to detect and delineate individual tree crowns and crown clusters. The method was implemented on a WorldView-2 image acquired over the parklands of Saponé, Burkina Faso, and rigorously evaluated against field reference data. The overall detection rate was 85.4% for individual tree crowns and crown clusters, with lower accuracies in areas with high tree density and dense understory vegetation. The overall delineation error (expressed as the difference between area of delineated object and crown area measured in the field) was 45.6% for individual tree crowns and 61.5% for crown clusters. Delineation accuracies were higher for medium (35–100 m2) and large (>100 m2) trees compared to small (<35 m2) trees. The results indicate potential of GEOBIA and WorldView-2 imagery for tree crown mapping in parkland landscapes and similar woodland areas.