Climate change means we will have to radically rethink how we use our landscapes

British landscapes are likely to change profoundly in the coming decades as global temperatures rise. Heiko Balzter explains why the country needs a coherent landscape strategy for adapting to the new climatic conditions and reducing our climate footprint

Detection of Amazon Forest Degradation Caused by Land Use Changes

Field and satellite optical methods for estimation of chlorophyll content were applied in three study sites of the Ecuadorian Amazon rainforest. Those sites represent a wide range of land use disturbance in secondary and pristine lowland rainforest. The first field method is based on transmittance from the SPAD-502 chlorophyll meter index, the second field method is based on reflectance measurements collected by a spectroradiometer, and the third method estimates chlorophyll content from the PROSPECT radiative transfer model. For the first method, seven models that account for a wide range of vegetation species showed similar average leaf chlorophyll contents until 80 units of SPAD-502. An average of the results of these models was computed and used as ground truth from where a generalized second-order polynomial model was created. For the second method, five chlorophyll indices based on reflectance measurements provided similar chlorophyll content estimations for all SPAD range (15–95 units). The third method estimates chlorophyll content based on the inversion process of the PROSPECT model. The satellite methods estimate vegetation indices sensitive to chlorophyll content from space. All methods have shown to be an alternative approach to detect forest degradation at local and regional levels caused by forest disturbances and land use changes

Multi-Scale Entropy Analysis as a Method for Time-Series Analysis of Climate Data

Evidence is mounting that the temporal dynamics of the climate system are changing at the same time as the average global temperature is increasing due to multiple climate forcings. A large number of extreme weather events such as prolonged cold spells, heatwaves, droughts and floods have been recorded around the world in the past 10 years. Such changes in the temporal scaling behaviour of climate time-series data can be difficult to detect. While there are easy and direct ways of analysing climate data by calculating the means and variances for different levels of temporal aggregation, these methods can miss more subtle changes in their dynamics. This paper describes multi-scale entropy (MSE) analysis as a tool to study climate time-series data and to identify temporal scales of variability and their change over time in climate time-series. MSE estimates the sample entropy of the time-series after coarse-graining at different temporal scales. An application of MSE to Central European, variance-adjusted, mean monthly air temperature anomalies (CRUTEM4v) is provided. The results show that the temporal scales of the current climate (1960–2014) are different from the long-term average (1850–1960). For temporal scale factors longer than 12 months, the sample entropy increased markedly compared to the long-term record. Such an increase can be explained by systems theory with greater complexity in the regional temperature data. From 1961 the patterns of monthly air temperatures are less regular at time-scales greater than 12 months than in the earlier time period. This finding suggests that, at these inter-annual time scales, the temperature variability has become less predictable than in the past. It is possible that climate system feedbacks are expressed in altered temporal scales of the European temperature time-series data. A comparison with the variance and Shannon entropy shows that MSE analysis can provide additional information on the statistical properties of climate time-series data that can go undetected using traditional method

Political Youth Education in Germany. Presenting a Qualitative Study on its Biographically Long-Term Effects

Purpose: There is an impact analysis presented, which explores the long-term effect of extra-curricular political youth education from the perspective of participants. Methods: The former participants retell their own education and life stories about five years later. Life stories were then interpreted in the course of research workshops and reconstructed as individual studies. Findings: The impact analysis generated a typology of biographical sustainability and shows the effects of political youth education by means of single case analysis and case comprehensive topics

Post-fire vegetation phenology in Siberian burn scars

Softwarefirma startext entwickelt Lösung zur Unterstützung der Bergungsarbeiten. "..... In einem ersten Schritt wird der Inhalt der Bergungskisten erfasst und deren Lagerort verzeichnet. Anschließend werden die einzelnen Inhalte, sogenannte Einheiten, detailliert beschrieben und zusätzlich durch ein Digitalfoto belegt. Für diese Arbeitsschritte hat startext gemeinsam mit den verantwortlichen Archivaren aus Köln innerhalb von nur zwei Wochen ein webbasiertes Softwareprogramm konzipiert und en..

Validation of Envisat MERIS algorithms for chlorophyll retrieval in a large, turbid and optically-complex shallow lake

The 10-year archive of MEdium Resolution Imaging Spectrometer (MERIS) data is an invaluable resource for studies on lake system dynamics at regional and global scales. MERIS data are no longer actively acquired but their capacity for global scale monitoring of lakes from satellites will soon be re-established through the forthcoming Sentinel-3 Ocean and Land Colour Instrument (OLCI). The development and validation of in-water algorithms for the accurate retrieval of biogeochemical parameters is thus of key importance if the potential of MERIS and OLCI data is to be fully exploited for lake monitoring. This study presents the first extensive validation of algorithms for chlorophyll-a (chl-a) retrieval by MERIS in the highly turbid and productive waters of Lake Balaton, Hungary. Six algorithms for chl-a retrieval from MERIS over optically complex Case 2 waters, including band-difference and neural network architectures, were compared using the MERIS archive for 2007-2012. The algorithms were locally-tuned and validated using in situ chl-a data (n = 289) spanning the five year processed image time series and from all four lake basins. In general, both band-difference algorithms tested (Fluorescence Line Height (FLH) and Maximum Chlorophyll Index (MCI)) performed well, whereas the neural network processors were generally found to much less accurately retrieve in situ chl-a concentrations. The Level 1b FLH algorithm performed best overall in terms of chl-a retrieval (R2 = 0.87; RMSE = 4.19 mg m- 3; relative RMSE = 30.75%) and particularly at chl-a concentrations of ≥ 10 mg m- 3 (R2 = 0.85; RMSE = 4.81 mg m- 3; relative RMSE = 20.77%). However, under mesotrophic conditions (i.e., chl-a < 10 mg m- 3) FLH was outperformed by the locally-tuned FUB/WeW processor (relative FLH RMSE < 10 mg m- 3 = 57.57% versus relative FUB/WeW RMSE < 10 mg m- 3 = 46.96%). An ensemble selection of in-water algorithms is demonstrated to improve chl-a retrievals

Making landscape Decisions to Meet Net Zero Carbon: Pathways that consider ethics, socio-ecological diversity, and landscape functions.

Landscapes are an integral part of the net-zero challenge; not only are they carbon stores but they constitute the environments upon which humans develop their livelihoods, interact and shape their cultures. This report focuses on three key landscape types (agricultural, peatlands and forests), and the associated practices and impacts with particular relevance to the net zero carbon agenda. We have brought together perspectives from natural and social science, humanities, and the arts to understand and evaluate how modern landscapes can absorb the impact of potential zero-carbon policies

Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion

The characterization of carbon stocks and dynamics at the national level is critical for countries engaging in climate change mitigation and adaptation strategies. However, several tropical countries, including Kenya, lack the essential information typically provided by a complete national forest inventory. Here we present the most detailed and rigorous national-scale assessment of aboveground woody biomass carbon stocks and dynamics for Kenya to date. A non-parametric random forest algorithm was trained to retrieve aboveground woody biomass carbon (AGBC) for the year 2014 ± 1 and forest disturbances for the 2014–2017 period using in situ forest inventory plot data and satellite Earth Observation (EO) data. The ecosystem carbon cycling of Kenya’s forests and wooded grassland were assessed using a model-data fusion framework, CARDAMOM, constrained by the woody biomass datasets from this study as well as time series information on leaf area, fire events and soil organic carbon. Our EO-derived AGBC stocks were estimated as 140 Mt C for forests and 199 Mt C for wooded grasslands. The total AGBC loss during the study period was estimated as 1.89 Mt C with a dispersion below 1%. The CARDAMOM analysis estimated woody productivity to be three times larger in forests (mean = 1.9 t C ha−1 yr−1) than wooded grasslands (0.6 t C ha−1 yr−1), and the mean residence time of woody C in forests (16 years) to be greater than in wooded grasslands (10 years). This study stresses the importance of carbon sequestration by forests in the international climate mitigation efforts under the Paris Agreement, but emphasizes the need to include non-forest ecosystems such as wooded grasslands in international greenhouse gas accounting frameworks