The Northern Eurasia Earth Science Partnership: An Example of Science Applied to Societal Needs

Northern Eurasia, the largest landmass in the northern extratropics, accounts for ~20% of the global land area. However, little is known about how the biogeochemical cycles, energy and water cycles, and human activities specific to this carbon-rich, cold region interact with global climate. A major concern is that changes in the distribution of land-based life, as well as its interactions with the environment, may lead to a self-reinforcing cycle of accelerated regional and global warming. With this as its motivation, the Northern Eurasian Earth Science Partnership Initiative (NEESPI) was formed in 2004 to better understand and quantify feedbacks between northern Eurasian and global climates. The first group of NEESPI projects has mostly focused on assembling regional databases, organizing improved environmental monitoring of the region, and studying individual environmental processes. That was a starting point to addressing emerging challenges in the region related to rapidly and simultaneously changing climate, environmental, and societal systems. More recently, the NEESPI research focus has been moving toward integrative studies, including the development of modeling capabilities to project the future state of climate, environment, and societies in the NEESPI domain. This effort will require a high level of integration of observation programs, process studies, and modeling across disciplines

Linking scales and disciplines : an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management

CITATION: Berger, C. et al. 2019. Linking scales and disciplines : an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management. Climatic Change, 156:139–150, doi:10.1007/s10584-019-02544-0.The original publication is available at https://www.springer.com/journal/10584Southern Africa is particularly sensitive to climate change, due to both ecological and socioeconomic factors, with rural land users among the most vulnerable groups. The provision of information to support climate-relevant decision-making requires an understanding of the projected impacts of change and complex feedbacks within the local ecosystems, as well as local demands on ecosystem services. In this paper, we address the limitation of current approaches for developing management relevant socio-ecological information on the projected impacts of climate change and human activities.We emphasise the need for linking disciplines and approaches by expounding the methodology followed in our two consecutive projects. These projects combine disciplines and levels of measurements from the leaf level (ecophysiology) to the local landscape level (flux measurements) and from the local household level (socio-economic surveys) to the regional level (remote sensing), feeding into a variety of models at multiple scales. Interdisciplinary, multi-scaled, and integrated socio-ecological approaches, as proposed here, are needed to compliment reductionist and linear, scalespecific approaches. Decision support systems are used to integrate and communicate the data and models to the local decision-makers.https://link.springer.com/article/10.1007/s10584-019-02544-0Publisher's versio

Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda

<p>Abstract</p> <p>Background</p> <p>Assessing biomass is gaining increasing interest mainly for bioenergy, climate change research and mitigation activities, such as reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+). In response to these needs, a number of biomass/carbon maps have been recently produced using different approaches but the lack of comparable reference data limits their proper validation. The objectives of this study are to compare the available maps for Uganda and to understand the sources of variability in the estimation. Uganda was chosen as a case-study because it presents a reliable national biomass reference dataset.</p> <p>Results</p> <p>The comparison of the biomass/carbon maps show strong disagreement between the products, with estimates of total aboveground biomass of Uganda ranging from 343 to 2201 Tg and different spatial distribution patterns. Compared to the reference map based on country-specific field data and a national Land Cover (LC) dataset (estimating 468 Tg), maps based on biome-average biomass values, such as the Intergovernmental Panel on Climate Change (IPCC) default values, and global LC datasets tend to strongly overestimate biomass availability of Uganda (ranging from 578 to 2201 Tg), while maps based on satellite data and regression models provide conservative estimates (ranging from 343 to 443 Tg). The comparison of the maps predictions with field data, upscaled to map resolution using LC data, is in accordance with the above findings. This study also demonstrates that the biomass estimates are primarily driven by the biomass reference data while the type of spatial maps used for their stratification has a smaller, but not negligible, impact. The differences in format, resolution and biomass definition used by the maps, as well as the fact that some datasets are not independent from the reference data to which they are compared, are considered in the interpretation of the results.</p> <p>Conclusions</p> <p>The strong disagreement between existing products and the large impact of biomass reference data on the estimates indicate that the first, critical step to improve the accuracy of the biomass maps consists of the collection of accurate biomass field data for all relevant vegetation types. However, detailed and accurate spatial datasets are crucial to obtain accurate estimates at specific locations.</p

Satellite-based terrestrial production efficiency modeling

Production efficiency models (PEMs) are based on the theory of light use efficiency (LUE) which states that a relatively constant relationship exists between photosynthetic carbon uptake and radiation receipt at the canopy level. Challenges remain however in the application of the PEM methodology to global net primary productivity (NPP) monitoring. The objectives of this review are as follows: 1) to describe the general functioning of six PEMs (CASA; GLO-PEM; TURC; C-Fix; MOD17; and BEAMS) identified in the literature; 2) to review each model to determine potential improvements to the general PEM methodology; 3) to review the related literature on satellite-based gross primary productivity (GPP) and NPP modeling for additional possibilities for improvement; and 4) based on this review, propose items for coordinated research

Ein Algorithmus zur wissensbasierten Klassifikation multitemporaler Radar-Fernerkundungsdaten

In dieser Arbeit wird ein Algorithmus vorgestellt, der die Nachteile einer pixelweisen Klassifikation umgeht, indem durch die Verwendung digitaler Feldgrenzen ein objektbasierter Ansatz verfolgt wird. Dabei wird ein texturbasierter Ansatz einem pixelbasierten gegen- uebergestellt. Darueberhinaus wird vorhandenes Vorwissen im Klassi- fikationsprozess beruecksichtigt

A fractional vegetation cover remote sensing product on pan-arctic scale with link to GeoTIFF image

The paper presents first results of a pan-boreal scale land cover harmonization and classification. A methodology is presented that combines global and regional vegetation datasets to extract percentage cover information for different vegetation physiognomy and barren for the pan-arctic region within the ESA Data User Element Permafrost. Based on the legend description of each land cover product the datasets are harmonized into four LCCS (Land Cover Classification System) classifiers which are linked to the MODIS Vegetation Continuous Field (VCF) product. Harmonized land cover and Vegetation Continuous Fields products are combined to derive a best estimate of percentage cover information for trees, shrubs, herbaceous and barren areas for Russia. Future work will concentrate on the expansion of the developed methodology to the pan-arctic scale. Since the vegetation builds an isolation layer, which protects the permafrost from heat and cold temperatures, a degradation of this layer due to fire strongly influences the frozen conditions in the soil. Fire is an important disturbance factor which affects vast processes and dynamics in ecosystems (e.g. biomass, biodiversity, hydrology, etc.). Especially in North Eurasia the fire occupancy has dramatically increased in the last 50 years and has doubled in the 1990s with respect to the last five decades. A comparison of global and regional fire products has shown discrepancies between the amounts of burn scars detected by different algorithms and satellite data

Modular designed Apps - an opportunity to standardize data collection methods and to encourage the reuse of software

Data collection strategies and the quality of the collection vary for parameters that are measured or observed in different citizen science projects. This makes it difficult to merge data of the same kind from different projects and thus hampers the reuse of data. Modular designed and customizable applications for mobile devices (Apps) represent a framework that can help to foster the standardization of data sampling methods and strategies. At the same time, they provide enough flexibility to be adjusted for the use in various scenarios. In this contribution, a corresponding framework is presented by the concept and the structure of the FieldMApp

Potential of Recurrence Metrics from Sentinel-1 Time Series for Deforestation Mapping

The REDD+ framework requires accurate estimates of deforestation. These are derived by ground measurements supported by methods based on remote sensing data to automatically detect and delineate deforestations over large areas. In particular, in the tropics, optical data is seldom available due to cloud cover. As synthetic aperture radar (SAR) data overcomes this limitation, we performed a separability analysis of two statistical metrics based on the Sentinel-1 SAR backscatter over forested and deforested areas. We compared the range between the 5th and 95th temporal percentiles (PRange) and the recurrence quantification analysis (RQA) Trend metric. Unlike the PRange, the RQA Trend considers the temporal order of the SAR data acquisitions, thus contrasting between dropping backscatter signals and yearly seasonalities. This enables the estimation of the timing of deforestation events. We assessed the impact of polarization, acquisition orbit, and despeckling on the separability between forested and deforested areas and between different deforestation timings for two test sites in Mexico. We found that the choice of the orbit impacts the detectability of deforestation. In all cases, VH data showed a higher separability between forest and deforestations than VV data. The PRange slightly outperformed RQA Trend in the separation between forest and deforestation. However, the RQA Trend exceeded the PRange in the separation between different deforestation timings. In this study, C-Band backscatter data was used, although it is commonly not considered as the most suitable SAR dataset for forestry applications. Nevertheless, our approach shows the potential of dense C-Band backscatter time series to support the REDD+ framewor