•The study aimed to predict outbreak potential of bark beetle based on detection different vitality stages of spruce.•Hyperspectral data are able to detect changes in biochemical–biophysical ...vegetation characteristics in spruce forest.•Important spectral information for derivation vitality status of spruce are 450–890nm.•We found a classification accuracy of 64.04% between C3 – infestation 2010 and C5 – healthy.•Hyperspectral data with grain of 4m contain relevant information to estimate differences in vitality of spruce than 7m.
The bark beetle (Ips typographus L.) is known for the detrimental impact it can have on Europe’s mature spruce forests with bark beetle outbreaks already having devastated thousands of hectares of spruce forests in Germany. This study analysed the hypothesis that the vitality of spruce vegetation is already susceptible from factors such as climate change or emissions to a certain extent before infestation, so that the role of the subsequent bark beetle infestation is only secondary.
Hyperspectral remote-sensing techniques were used to detect changes in biochemical–biophysical vegetation characteristics in the spruce forest of the Bavarian Forest National Park, Germany. For this study, several spectral bands, vegetation indices and specific spectral band combinations of hyperspectral HyMAP remote-sensing data with a 4m and a 7m ground resolution were analysed and compared in terms of their classification accuracy, generating an ID3 decision tree.
The vitality classes and thus also the attack stages of the spruce vegetation could be estimated with moderate to good accuracy using hyperspectral remote-sensing data. Clear spectral differences between the class with spruce trees that were still green but with reduced vitality (possibly the first stages of green-attack) and the class with healthy spruce trees could be ascertained. The best spectral characteristics, spectral indicators and spectral derivatives related to vitality classes and thus attack stages were typically based on wavebands related to prominent chlorophyll absorption features in the VI within the spectral range of 450–890nm. Only limited spectral information and derivatives could be found in the short-wave infrared region 1 (SWIR) within the spectral range of 1400–1800nm, which reflects the water content of the spruce needles. The class of spruce trees that were still green but with reduced vitality (possibly the first stages of green-attack) showed a trend towards detectability and differentiation with spectral indicators and index derivatives. However, the prediction of observed effects with 64% accuracy as observed here is regarded as insufficient in forestry practises. Hyperspectral data with a ground resolution of 4m were found to contain more information relevant to estimating the vitality class of spruce vegetation compared to hyperspectral data with a ground resolution of 7m.
•We provide a comprehensive overview on earth observation (EO) indicators for biodiversity (BD).•We focus on taxonomic, structural and functional biodiversity.•EO is not able to record BD according ...to taxonomical classifications of in-situ species.•Spectral traits (ST) and spectral trait variations (STV) are the basis concept of EO to quantify BD.•Coupling different approaches, developing sensor networks and new concepts, tools and models in handling complex and big data are important.
Impacts of human civilization on ecosystems threaten global biodiversity. In a changing environment, traditional in situ approaches to biodiversity monitoring have made significant steps forward to quantify and evaluate BD at many scales but still, these methods are limited to comparatively small areas. Earth observation (EO) techniques may provide a solution to overcome this shortcoming by measuring entities of interest at different spatial and temporal scales.
This paper provides a comprehensive overview of the role of EO to detect, describe, explain, predict and assess biodiversity. Here, we focus on three main aspects related to biodiversity − taxonomic diversity, functional diversity and structural diversity, which integrate different levels of organization − molecular, genetic, individual, species, populations, communities, biomes, ecosystems and landscapes. In particular, we discuss the recording of taxonomic elements of biodiversity through the identification of animal and plant species. We highlight the importance of the spectral traits (ST) and spectral trait variations (STV) concept for EO-based biodiversity research. Furthermore we provide examples of spectral traits/spectral trait variations used in EO applications for quantifying taxonomic diversity, functional diversity and structural diversity. We discuss the use of EO to monitor biodiversity and habitat quality using different remote-sensing techniques. Finally, we suggest specifically important steps for a better integration of EO in biodiversity research.
EO methods represent an affordable, repeatable and comparable method for measuring, describing, explaining and modelling taxonomic, functional and structural diversity. Upcoming sensor developments will provide opportunities to quantify spectral traits, currently not detectable with EO, and will surely help to describe biodiversity in more detail. Therefore, new concepts are needed to tightly integrate EO sensor networks with the identification of biodiversity. This will mean taking completely new directions in the future to link complex, large data, different approaches and models.
In most parts of the world, land-use/land cover can be considered an interface between natural conditions and anthropogenic influence. Indicators are being sought which reflect landscape conditions, ...pressures and related societal responses. Landscape metrics, which are based on the number, size, shape and arrangement of patches of different land-use/land cover types, are used-together with areal statistics-to quantify landscape structure and composition.
The applicability of landscape metrics for landscape monitoring has been investigated in a 700 km
2 test region in eastern Germany, where open cast coal mining has caused far reaching land-use changes in the course of this century. Time series of maps (1912–2020) have been elaborated from various data sources (topographic maps, aerial photography, satellite images, prospective planning material). Landscape metrics have been calculated for the entire test region and for ecologically defined subregions at the landscape, class and patch level.
The results are presented and methodological issues are addressed, namely the impact of scale, spatial and temporal resolution on the interpretability of landscape metrics. Critical issues are:
•
the application of remote sensing methods, which is a pre-requisite for the area-wide monitoring of land-use change;
•
standardised data processing techniques, which are vital for the spatial and temporal comparability of results;
•
the selection of a manageable set of indicators which embraces the structural properties of landscapes;
•
the choice of appropriate spatial units which allow for an integration of landscape indicators (which tend to relate to cross-border phenomena) and socio-economic indicators (which are usually available for administrative entities or areas).
These issues are discussed in relation to the application of landscape indices in environmental monitoring.
A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and ...assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic‐abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.
Plain Language Summary
A reanalysis is a unique set of continuous variables produced by optimally merging a numerical model and observed data. The data are merged with the model using available uncertainty estimates to generate the best possible estimate of the target variables. The framework for generating a reanalysis consists of the model, the data, and the model‐data‐fusion algorithm. The very specific requirements of reanalysis frameworks have led to the development of Earth‐compartment specific reanalysis for the atmosphere, the ocean and land. Here, we review atmospheric and oceanic reanalyzes, and in more detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic, and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Based on a review of existing achievements, we identify five major steps required to develop reanalysis for terrestrial ecosystem to shed more light on biotic and abiotic interactions. In the future, terrestrial ecosystem reanalysis will deliver continuous data streams on the state and the development of terrestrial ecosystems.
Key Points
Reanalyzes provide decades‐long model‐data‐driven harmonized and continuous data sets for new scientific discoveries
Novel global scale reanalyzes quantify the biogeochemical ocean cycle, terrestrial carbon cycle, land surface, and hydrologic processes
New observation technology and modeling capabilities allow in the near future production of advanced terrestrial ecosystem reanalysis
Climate change, urbanisation and demographic change affect urban areas and pose a range of health-related challenges to urban residents, including heat waves, drought periods, air pollution and ...densification processes. Urban green spaces provide ecosystem services that can help to mitigate the effects of these challenges. Urban green spaces such as parks, urban gardens and street trees regulate the microclimate and buffer noise as well as a variety of air pollutants. Parks promote physical activity, relaxation and social interaction. The potential to provide these services might be limited during extreme weather events such as heat waves and drought periods. With this experience-based perspective paper, we introduce an interdisciplinary project that consists of multi-method field campaigns to assess the potential of urban parks to provide regulating and recreational ecosystem services in the context of the 2018 and 2019 heat and drought periods in Germany. We highlight that multi-method field campaigns that combine sensor-based environmental measurements with social science approaches, including visitor observations, counts, and questionnaire surveys, are highly useful when urbanisation and climate change-related challenges must be effectively addressed in the context of the complex socio-ecological systems of a city. Based on our hands-on experiences, we provide recommendations for local urban green space planning and outline prospects for future research.
The purpose of this study was to seek radiation dose responses separately for primary hepatocellular carcinoma (HCC) and metastatic (MET) colorectal liver tumours to establish tumour control ...probabilities (TCPs) for radiotherapy (RT) of liver tumours.
The records of 36 HCC and 26 MET colorectal liver tumour patients were reviewed. The median dose per fraction and total dose were 4 Gy (2-10 Gy) and 52 Gy (29-83 Gy) for the HCC group and 3.6 Gy (2.0-13.0 Gy) and 55 Gy (30-80 Gy) for the MET group, respectively. Median tumour diameter was 6.6 cm (3.0-18.0 cm) and 5.0 cm (1.0-13.0 cm) for the HCC and MET groups, respectively. A logistic TCP model was fitted to the response data for each group using the maximum likelihood method.
50% and 90% probabilities of 6-month local control were estimated to be achievable by 2 Gy per fraction equivalent doses (α/β=10 Gy) of 53 Gy and 84 Gy for the HCC group and 70 Gy and 95 Gy for the MET group, respectively. Actuarial 1-year local control for the HCC and MET groups was 65% (45-85%) and 32% (6-58%), respectively, whereas median time to failure was 543 days (374-711 days) and 183 days (72-294 days), respectively.
Dose-response relationships were found and modelled for the HCC and MET patient groups, with a higher dose required to control MET tumours. RT offers better local control for HCC than for MET colorectal liver tumours at our institution.
An improved understanding of radiation dose-response relationships for primary and MET colorectal liver tumours will help inform future dose prescriptions.
Landscape monitoring usually relies on land-use statistics which reflect the share of land-sue/land cover types. In order to understand the functioning of landscapes, landscape pattern must be ...considered as well. Indicators which address the spatial configuration of landscapes are therefore needed. The suitability of landscape metrics, which are computed from the type, geometry and arrangement of patches, is examined. Two case studies in a surface mining region show that landscape metrics capture landscape structure but are highly dependent on the data model and on the methods of data analysis. For landscape metrics to become part of policy-relevant sets of environmental indicators, standardised procedures for their computation from remote sensing images must be developed.
Purpose: To modify the single-threshold parametric response map (ST-PRM) method for predicting treatment outcomes in order to facilitate its use for guidance of adaptive dose painting in ...intensity-modulated radiotherapy. Methods: Multiple graded thresholds were used to extend the ST-PRM method (Nat. Med. 2009;15(5):572-576) such that the full functional change distribution within tumours could be represented with respect to multiple confidence interval estimates for functional changes in similar healthy tissue. The ST-PRM and graded-threshold PRM (GT-PRM) methods were applied to functional imaging scans of 5 patients treated for hepatocellular carcinoma. Pre and post-radiotherapy arterial blood flow maps (ABF) were generated from CT-perfusion scans of each patient. ABF maps were rigidly registered based on aligning tumour centres of mass. ST-PRM and GT-PRM analyses were then performed on overlapping tumour regions within the registered ABF maps. Main findings: The ST-PRMs contained many disconnected clusters of voxels classified as having a significant change in function. While this may be useful to predict treatment response, it may pose challenges for identifying boost volumes or for informing dose-painting by numbers strategies. The GT-PRMs included all of the same information as ST-PRMs but also visualized the full tumour functional change distribution. Heterogeneous clusters in the ST-PRMs often became more connected in the GT-PRMs by voxels with similar functional changes. Conclusions: GT-PRMs provided additional information which helped to visualize relationships between significant functional changes identified by ST-PRMs. This may enhance ST-PRM utility for guiding adaptive dose painting.
The assessment and quantification of spatio-temporal soil characteristics and moisture patterns are important parameters in the monitoring and modeling of soil landscapes. Remote-sensing techniques ...can be applied to characterize and quantify soil moisture patterns, but only when dealing with bare soil. For soils with vegetation, it is only possible to quantify soil-moisture characteristics through indirect vegetation indicators, i.e. the “vitality” of plants. The “vitality” of vegetation is a sum of many indicators, whereby different stress factors can induce similar changes to the biochemical and physiological characteristics of plants. Analysis of the cause and effect of soil-moisture properties, patterns and stress factors can therefore only be carried out using an experimental approach that specifically separates the causes. The study describes an experimental approach and the results from using an imaging hyperspectral sensor AISA-EAGLE (400–970 nm) and a non-imaging spectral sensor ASD (400–2,500 nm) under controlled and comparable conditions in a laboratory to study the spectral response compared to biochemical and biophysical vegetation parameters (“vitality”) as a function of soil moisture characteristics over the entire blooming period of Ash trees. At the same time that measurements were taken from the hyperspectral sensors, the following vegetation variables were also recorded: leaf area index (LAI), chlorophyll meter value — SPAD-205, vegetation height, C/N content and leaf water content as indicators of the “vitality” and the state of the vegetation. The spectrum of each hyperspectral image was used to calculate a range of vegetation indices (VI’s) with relationships for soil moisture characteristics and stress factors. The relationship between vegetation indices and plant “vitality” indicators was analysed using a Generalized Additive Model (GAM). The results show that leaf water content is the most appropriate vegetation indicator for assessing the “vitality” of vegetation. With the Water Index (WI) it was possible to differentiate between the moisture treatments of the control, moisture drought stress and the moisture flooding treatment over the entire growing season of the plants (R² = 0.94). There is a correlation between the “vitality” vegetation parameters (LAI, C/N content and vegetation height) and the indicators NDVI, WI, PRI and Vog2. In our study with Ash trees the vegetation parameter chlorophyll was found not to be a suitable indicator for detecting the “vitality” of plants using the spectral indicators. There is a possibility that the sensitivity of the indicators selected was too low compared to changes in the chlorophyll content of Ash trees. Adding the co-variable ‘time’ strengthens the correlation, whereas incorporating time and moisture treatment only improves the model very slightly. This shows that changes to the biochemical and biophysical characteristics caused by phenology, overlay a differentiation of the moisture treatments.
Purpose: To investigate the effects of registration error (RE) on parametric response map (PRM) analysis of pre and post-radiotherapy (RT) functional images. Methods: Arterial blood flow maps (ABF) ...were generated from the CT-perfusion scans of 5 patients with hepatocellular carcinoma. ABF values within each patient map were modified to produce seven new ABF maps simulating 7 distinct post-RT functional change scenarios. Ground truth PRMs were generated for each patient by comparing the simulated and original ABF maps. Each simulated ABF map was then deformed by different magnitudes of realistic respiratory motion in order to simulate RE. PRMs were generated for each of the deformed maps and then compared to the ground truth PRMs to produce estimates of RE-induced misclassification. Main findings: The percentage of voxels misclassified as decreasing, no change, and increasing, increased with RE For all patients, increasing RE was observed to increase the number of high post-RT ABF voxels associated with low pre-RT ABF voxels and vice versa. 3 mm of average tumour RE resulted in 18-45% tumour voxel misclassification rates. Conclusions: RE induced misclassification posed challenges for PRM analysis in the liver where registration accuracy tends to be lower. Quantitative understanding of the sensitivity of the PRM method to registration error is required if PRMs are to be used to guide radiation therapy dose painting techniques.
PDF
