“Monitoring and risk index measuring system based on measured ecosystem services as a function of sectoral economic performance, and corresponding method” in Patent Application Approval Process (USPTO 20230068107): Swiss Reinsurance Company Ltd.

2023 MAR 16 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors DE JONG, Rogier (Zurich, CH); RETSA, Anna (Zurich, CH); SCHELSKE, Oliver (Zurich, CH), filed on October 17, 2022, was made available online on March 2, 2023, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application is assigned to Swiss Reinsurance Company Ltd. (Zurich, Switzerland).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Global measurement and monitoring of biodiversity and ecosystem services (BES) is an important, but technically challenging task, as human activities alter the functioning of ecosystems and the structure and composition of biological populations at all taxonomic levels. To stem biodiversity loss and maintain important ecosystem services it is necessary to be able to measure and monitor the rate, the extent and the geography of these changes. In view of the extent of the necessary measures, knowledge about global changes in biodiversity and ecosystems is still limited. Moreover, what is known about the changes in biodiversity is immensely complicated by taxonomic, geographic and temporal distortions. At present, novel systems are being developed for monitoring biodiversity in order to be able to measure, monitor and assess systematically, changes over multiple taxa in large volumes. For monitoring changes in species, communities and ecosystems, various globally consistent metrics have been proposed over the course of time. These metrics are biological, they react sensitively to changes, are ecosystem-independent and have the goal of making uniform monitoring protocols possible worldwide. These efforts have been furthered by increasing access to globally available in-situ biodiversity observations. However, as in-situ data alone are not sufficient for assessing global patterns of diversity, supplementary measured data are required in order to make effective monitoring measures possible.

“Earth observations (e.g. Earth Observation (EO) measured data from satellite or aerial photographs) supplement in-situ measured data, by providing repeatable, thematically consistent and spatially continuous measurements of terrestrial ecosystems, which can characterize and measure patterns of biodiversity over large, insufficiently investigated regions. However, establishing the link between field and EO data is a technically difficult challenge. This includes the evaluation of incomplete samples (e.g. if the field measurements do not adequately reflect the extent of the environmental variations) and adjustment for differences in size and scale (e.g. if field plots are much smaller than EO pixels). Development of EO-based biodiversity monitoring systems requires technically a comprehensive approach for correlating these data.

“Scaling plays a central role both in the in-situ measurement of ecological processes and in EO measuring techniques. The technical linking of spatial and temporal scales in biological communities is then a central problem in the technical area and is designated as the problem of pattern and scale. The problem of pattern and scale emphasizes that often several ecological processes determine the pattern of biodiversity, and that these processes may often act over several spatial and organismic scales. Therefore there is seldom a single measuring scale that is the most suitable for measuring and detecting how particular processes control patterns. Similar scale dependencies apply to EO measurements, and the granularity of an EO sensor often determines which patterns can be measured, and only multiple-scale EO analyses can uncover the effects of multiple processes and determine the biodiversity pattern. The application of concepts for patterns and scales to EO measurements could be a means for better linking of these regions and for providing a path for improved monitoring of biodiversity.

“The technical problem of patterns and orders of magnitude in ecological measuring systems is concentrated on two different measuring scales, namely granularity and extent. In the following, unless defined otherwise for these scales, they relate to scales in the spatial sense, although the temporal granularity is also described by the frequency of observations (e.g. a diurnal cycle for the net primary productivity) and the temporal extent of the total time may describe in what time interval an ecological process can take place (e.g. phenological variation over the course of a year).

“Measuring scales must moreover be selected correctly, in order to be able to measure/detect biodiversity patterns (i.e. biodiversity) or ecological processes in a particular scale or a series of scales. An important dynamic in scaling is that with a change in the scale of the measurement, the variation within this measurement is also subject to a change. For example if biodiversity/ecosystem functions suggest that the relation between species diversity and productivity should be concave and a maximum biomass accumulation applies at a medium diversity both for primary and for secondary productivity. However, this functional form is dependent on cell size and cannot be used for measuring ecological processes. Measurements of patterns at community level, such as species diversity and turnover (i.e. alpha and beta diversity), has been proven to vary directly with the scale.

“The measuring scales also form the technical limitation as to which biodiversity patterns can be measured by EO, i.e. by satellite-supported Earth observation (see FIG. 2). In general, fine-grained sensors can measure patterns at the species and community level, e.g. the occurrence and incidence of species and taxonomic diversity. The measurement of characters and corresponding properties of species has proved at least partially to be a technical challenge owing to the difficulties in differentiation of individual species in the EO images and EO measurements. However, certain characteristics of plants, such as the nitrogen content of tree tops and rates of photosynthesis, can even be measured at moderate granularity. High-frequency measurements can image temporally sensitive processes, such as the phenology of vegetation, but with high-frequency continuous measurements are often at the expense of coarser granularity. Coarse-grained EO sensors can measure patterns at the ecosystem level, such as disturbance regimens or the extent of ecosystems. Until now in the prior art, satellite-supported EO has concentrated on the measurement of patterns at the ecosystem level, but the increasing number of fine-grained EO sensors in orbit could extend EO biodiversity acquisition to patterns at the species and community level.

“Technically there are important similarities in scaling dynamics between field-measured data, i.e. in situ data, and EO data: granularity and extent limit both the variations between the grains. Large cells tend towards more species per plot and a smaller fluctuation between plots. Equally, large EO pixels tend to contain more organisms per grain and a smaller fluctuation between the grains, which alters the specificity of the measurement. The granularity of an EO sensor therefore restricts technically the smallest possible measuring unit. However, data can be aggregated spatially to larger scales. For example, continuous pixels, which measure the same tree, could be combined to a single crown, or clusters of forested pixels can be combined to delimit forest fragments. This makes comparisons possible between crowns or fragments instead of pixels and helps bridge the gap between spatial and biological scales. This object-based image analysis is now being used more and more frequently for monitoring biodiversity, through the development of novel segmentation algorithms, which are intended for EO measurements. Although this approach makes the processing of the measured data and the ecological interpretation of EO data easier, there are also important scaling dynamics here, which are altered with the aggregation of data over scales. A technical challenge for the problem of pattern and scale is that there is seldom only one scale at which a pattern must be investigated, because often several ecological processes determine the spatial biodiversity pattern. These patterns must often be measured at several points along biological, spatial or temporal scale spectra, in order to detect how several processes control the pattern.

“In the prior art there are two fundamental methods for measuring biodiversity patterns by means of EO measurements. The first is the direct measurement of patterns at the level of species, communities or ecosystems. Examples of this paradigm are the Identification of individual organisms within a species or the acquisition of the extent of an ecosystem. Secondly, biodiversity patterns can be modelled indirectly using EO as starting parameters and environmental features for predictive measurements. Examples of this method are modelling of species diversity based on measurements of the habitat structure or modelling of the distribution of species based on land cover measurements.”

There is additional background information. Please visit full patent to read further.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “The object of the present invention is to provide a new measuring and monitoring system and method, for measuring and monitoring complex, nature-based, and geographically distributed systems, in particular for physical measurement of biodiversity and ecosystem services and correlation thereof with technical and economic processes and activities. A further aim of the invention is to provide a new measuring and monitoring system and method not only for measuring changes in ecosystems and biodiversity, but also in quantitative measurement of resultant risks within the monitored ecosystem.

“According to the present invention, these aims are achieved in particular by the features of the independent claims. In addition, further advantageous embodiments follow from the dependent claims, the description and the figures.

“According to the present invention, the aforementioned objects for measuring and monitoring systems for measuring and monitoring ecosystem and biodiversity services and associated risks and the corresponding methods are achieved in that the measuring and monitoring system comprises a plurality of measuring sensors and measuring devices, by means of which geographically cellularly delimited measuring parameters are captured, wherein the measuring sensors and measuring devices comprise in-situ measuring devices and/or EO (Earth Observation) measuring devices for measurement/acquisition of values of atmospheric measuring parameters and/or values of maritime measuring parameters and/or values of land-based measuring parameters, in that the measuring and monitoring system comprises a central digital platform with a core engine and a persistent memory, wherein by means of an aggregation module based on predefined parameterizations of BES indicators, measuring parameters for measuring the BES indicators are acquired by measuring sensors and measuring devices and are stored in the persistent memory, wherein the measuring parameter can be aggregated by parameterization of the BES indicators to quantitative values of the BES index for each of the BES indicators and/or a total value of BES index, so that the system comprises a correlation module comprising a plurality of parameterized stored production processes and production outputs with assigned correlation measuring indices, wherein the respective correlation measuring index measures the dependency of a production process or production output on the individual BES indices in respective, defined subsectors using the average values for each BES indicator, and in that probability of occurrence values for the occurrence of damage events or reduction of production outputs based on the measured BES index values, weighted with respect to the production processes and production outputs by means of the assigned correlation measuring indices, are measured using the average value of the correlation measuring indices for the individual parameterized BES indicators and the aggregated dependence of all BES indicators contained in the cumulative BES index.”

The claims supplied by the inventors are:

“1. A measuring and monitoring system, comprising: a plurality of measuring sensors and measuring devices configured to acquire geographically cellularly delimited measuring parameters, the measuring sensors and measuring devices including in-situ measuring devices and/or EO (Earth Observation) measuring devices configured to measure values of atmospheric measuring parameters and/or values of maritime measuring parameters and/or values of land-based measuring parameters; and a central digital platform with a Core Engine and a persistent memory configured to: implement an aggregation module based on predefined parameterizations of BES (biodiversity and ecosystem services) indicators, the aggregation module storing measuring parameters acquired by the measuring sensors and measuring devices for measuring the BES indicators in the persistent memory and the measuring parameters being aggregated by parameterization of the BES indicators to quantitative values of a BES index for each of the BES indicators and/or a total value of a cumulative BES index, implement a correlation module including a plurality of parameterized stored production processes and production outputs with assigned correlation measuring indices, the correlation measuring indices measuring a dependency of a production process or a production output on the individual BES indices, respectively, and defined subsectors using average values for each of the BES indicators, determine and/or measure probability of occurrence values for an occurrence of damage events or reduction of production outputs on the basis of the BES index values, measured BES index values being weighted in relation to the production processes and production outputs using an average value of the correlation measuring indices for the individual parameterized BES indicators and an aggregated dependency of all of the BES indicators contained in the cumulative BES index, and determine and/or measure the probability of occurrence values for the occurrence of damage events or reduction of production outputs based on the measured and weighted, with respect to the production processes and production outputs using the correlation measuring indices, BES index values using the average value of the correlation measuring indices for the individual parameterized BES indicators and the aggregated dependency of all of the BES indicators contained in the cumulative BES index.

“2. The measuring and monitoring system according to claim 1, wherein the BES indicators include at least measuring parameters for measuring at least one of: habitat intactness, pollination, air quality and local climate, water security, water quality, soil fertility, erosion control, coastal protection, food provision, and timber provision.

“3. The measuring and monitoring system according to claim 2, wherein indicator substances are measured as quantitative indicators for the air quality by the in-situ measuring devices, and the indicator substances include at least one of: content of nitrogen dioxide, fine dust and soot content, grit content, a proportion of non-transparent/opaque particles, dust ingredients comprising pollen, and sea salts.

“4. The measuring and monitoring system according to claim 2, wherein a quantitative measurement of the water quality is measured by the in-situ measuring devices by measuring at least one value of: electrical conductivity, and dissolved substances including hormones and/or fungicides and/or pesticides.

“5. The measuring and monitoring system according to claim 1, wherein, for measurement, the BES indicators are selected and/or the selection thereof is varied until a defined measurement accuracy of one or more of the BES index values is attained.

“6. The measuring and monitoring system according to claim 1, wherein the correlation module is configured to measure the correlation measuring index of each production process or production output, and by means of the cumulative BES index, all correlated BES indicators are acquired as a weighted average based upon at least: (i) a measured, average correlation measuring index, (ii) a measured maximum correlation measuring index, and (iii) a measured number of the correlated BES indicators, on which a respective production process or production output depends.

“7. The measuring and monitoring system according to claim 2, wherein a correlation measuring index for the water security and the timber provision are assigned a double weighting for generating the cumulative BES index.

“8. The measuring and monitoring system according to claim 1, wherein the BES indicators contained in the BES indices are linked based on their measurement relevance to ENCORE (Exploring Natural Capital Opportunities, Risks and Exposure) services, are modified to their maximum measurability, and are weighted with respect to their correlation.

“9. The measuring and monitoring system according to claim 8, wherein the subsectors are based on a Global Industry Classification Standard (GICS) of ENCORE.

“10. The measuring and monitoring system according claim 1, wherein the subsectors include two or more hierarchic levels based on the NACE Rev2 (Statistical Classification of Economic Activities in the European Community) industry classification.

“11. The measuring and monitoring system according to claim 10, wherein for dependencies based on the classifications, discrete dependency values with values “slight”, “moderate,” and “high” are classified and discretized using terciles, and a dependency on sectors that lie in the upper tercile is assigned as “high” and a dependency on sectors that lie in the lower tercile is assigned as “low”.

“12. The measuring and monitoring system according to claim 11, wherein, for generating the dependency, further measuring parameters for measuring economic indicators are selected.

“13. The measuring and monitoring system according to claim 12, wherein the economic indicators are selectable at least partially from the economic indicators of Oxford Economics for various countries, and when the “value-added output in % of GDP” is selected, a contribution of each economic sector to the GDP is acquired by measurement and for each economic sector and each country, a weighted total of a dependency of the economic sectors on the BES index is generated and the weights comprise the measured share of each sector in the GDP of the country.

“14. The measuring and monitoring system according to claim 11, wherein the weighted dependency based on the terciles is classifiable in low, medium, and high.

“15. The measuring and monitoring system according to claim 2, wherein parameter data of the BES indicators of the food provision individually or with aggregation together are be aggregated on a BES index of the timber provision at all spatial levels for each of the BES indicators and are benchmarkable compared to others.

“16. The measuring and monitoring system according to claim 15, wherein the spatial levels include at least one of: towns, countries, cantons, federal states, nations, supranational entities, and spatial levels as NUTS-Il.

“17. The measuring and monitoring system according to claim 16, wherein the supranational entities include the European Union (EU).

“18. The measuring and monitoring system according to claim 1, wherein the measurements for repeated measurement of the BES indicators and of the aggregation of the BES indicator include time series and/or regular monitoring.

“19. The measuring and monitoring system according to claim 1, wherein the central digital platform is configured to implement a multi-location analyses at a level of concrete GIS coordinates as unique address or km² or zip codes and/or postcodes for identifying vulnerabilities in a value-added chain of enterprises against BES loss based on available company data.

“20. The measuring and monitoring system according to claim 19, wherein locations of the multi-location analyses include 1st tier and/or 2nd tier and/or 3rd tier suppliers.

“21. The measuring and monitoring system according to claim 19, wherein the multi-location analyses is carried out in conjunction with a dependency of an entrepreneurial activity and/or of a supplier based on the NACE classification, which is unambiguous for all entrepreneurial activity and the underlying dependency of the NACE classified activity on the BES indicators.

“22. The measuring and monitoring system according to claim 19, wherein the multi-location analyses includes aggregating data at an overall level of an enterprise and benchmarking compared to others.

“23. The measuring and monitoring system according to claim 22, wherein the overall level of the enterprise comprises all production facilities including those of suppliers.”

There are additional claims. Please visit full patent to read further.

URL and more information on this patent application, see: DE JONG, Rogier; RETSA, Anna; SCHELSKE, Oliver. Monitoring and risk index measuring system based on measured ecosystem services as a function of sectoral economic performance, and corresponding method. U.S. Patent Application Number 20230068107, filed October 17, 2022 and posted March 2, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230068107)&db=US-PGPUB&type=ids

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