Perturbaciones forestales, riesgos, respuesta a desastres

Today, the technology for identifying bark beetle attacks in the forest is largely based on manual work through visual checks of forest areas, where early attacks are almost impossible to see, while old attacks are easier to detect. Attempts have been made to identify damage attacks using satellite radar maps which give a good indication and can be seen as a complementing part to our precision analysis. Arboair Forest Mapper is a service where you analyze your images via our AI. Our model is trained on over 200 000 trees and it is verified by forest managers.

Improved work safety through a sensor node network which connects to anyone carrying such a node and provides information about the current danger situation over a license-free band using Bluetooth low energy (BLE). For example: in a tree felling operation with a harvester which is supported by a forest worker, any person with such a little IT-device in his / her pocket (such as supervision personal, field forester, …) will get information about the position of the harvester and the work the harvester is doing. On the other hand, also the harvester has the information about these people. Risk alert warnings are sent to actors automatically, risk zones and risk status can be retrieved from actors, offenses of critical overlaps in risk safety zones are identified. The system is using u-Blox M8N GPS modules and map visualization on screens. The information gets translated to a danger situation depending on the individual work-situation (for example larger danger area when the harvester is cutting a tree than while driving). Communication of 150 - 700m, up to 9 slave nodes and battery allows 50h usage. GPS accuracy around 2.5m under forest conditions. The system may connect to a Cloud. This opens further data processing options, such as inclusion of passers-by via GSM-net or team-oriented data analysis for work-safety education needs. 

The threat of forests by various harmful microorganisms is growing due to changing climate conditions and spreading of non-native pathogens and pests.. Simultaneously the relevance of biological methods of monitoring and preventing forest degradation is increasing in the face of the chemical’s use restrictions. The main aim of the project is the development of an innovative device (electronic nose/ e-NOS), based on a matrix of broad-band electrochemical sensors and neural networks that would detect and analyse the odor-based signals e.g. pheromones of certain insect species. The examples of pathogens and pests addressed in the project include Dendrolimus Pini (L.) and Phytophthora oomycetes.
The developed system delivers comprehensive and complex information which allows to create a neural classifier (using artificial intelligence). The dedicated software was developed to perform the analysis of the data and create a database – library of signals, which will allow to detect the analytes sought in the field. For each application foreseen in the project (analysis of specific smells), dedicated sensory matrices were prepared.   

Cross-Forest está desarrollando una plataforma común de datos forestales abiertos y un modelo de datos transfronterizo (ontología) compartido entre Portugal y España, para la publicación de inventarios forestales, mapas y otras bases de datos forestales en formato Linked Open Data (LOD). Cross-Forest proporcionará un repositorio público (endpoint) de publicación de Datos Forestales según el modelo producido. El objetivo principal se centra en mantener la información forestal siempre disponible y actualizada para facilitar su explotación a todos los agentes implicados en la gestión e investigación forestal.

Se están desarrollando dos casos de uso:
CAMBRIC - orientado a simular la evolución de las masas forestales y la calidad de la madera en distintos escenarios de gestión forestal.
FRAME – enfocado a la predicción del comportamiento y propagación de los incendios forestales a través de información precisa sobre materiales combustibles, mapas forestales y modelos de propagación.

Se emplean recursos de computación de alto rendimiento (HPC) debido a la cantidad de datos generados y gestionados, y a la complejidad de los modelos.

Los resultados obtenidos hasta ahora demuestran la utilidad y versatilidad que proporciona la tecnología LOD, ya que permite a los usuarios acceder y gestionar libremente datos actualizados para desarrollar herramientas adaptadas a sus necesidades y propósitos. La publicación de datos como LOD permite a las Administraciones Públicas cumplir fácilmente con sus requisitos de transparencia y publicidad, optimizar recursos y llevar un control estadístico del uso de los datos públicos.

Cross-Forest is developing a common platform for open forest data, and a cross-border data model (ontology) shared between Portugal and Spain, for the publication of forest inventories, maps and other forest databases in Linked Open Data format (LOD). Cross-Forest will provide a public endpoint exposing Forest Data, according to the produced model. The main goal is focused on keeping forest information always available and updated, to make exploitation easier for all stakeholders involved in forest management and research.
Two use cases are being developed:
CAMBRIC - to estimate the evolution of forests and wood quality, under different management scenarios
FRAME - to predict forest fires behavior and spreading through precise information on combustible materials, forestry maps and propagation models.
High Performance Computing (HPC) resources are employed due to the amount of data generated and managed, and to the complexity of the models.
Results so far show the usefulness and versatility provided by LOD technology, as It allows users to freely access and manage updated data to develop tools adapted to their needs and purposes. Publishing data as LOD allows Public Administrations to easily fulfil their requirements of transparence and publicity, optimize resources and keep a statistic control of the use of public data.

Applications like forest inventory and forestry planning, planning of fellings, optimization of harvesting and accounting processes, improvements of timber logistic, evaluation of damaging events etc. are expecting a realistic virtual illustration of the real forest. Within this project the preconditions for building up a single central database – the Virtual Forest - describing the extensive area of North Rhine-Westphalia are constructed. This database contains a mathematical description of biological and technical aspects of the real forest in different detailing (e.g. forest and single-tree consideration) and different precision. Apart from available terrestrial data and a digital site classification, developed by the Landesbetrieb Wald und Holz NRW, the Virtual Forest is based on remote sensing data and information derived thereof. It applies newly developed algorithms on well-known data formats: for single tree identification laser data and aerial images are used, tree species classification uses images and area segmentation is based on raster and vector data. Thus, a comprehensive survey ranging from inventory to forest growth to logistics can be provided. The Virtual Forest provides all stored data in a new 4D geo-data infrastructure while using standardised interfaces. All data (basic data, derived data, technical data, etc.) can also be used by third (programmes, users). Furthermore, a meta-data catalogue answers questions as “Which data of a certain area and/or to a specific subject are placed in which format, which exactness and which actuality at what costs at which place?” Appropriate safety concepts secure the data access as well as the data itself. The consequent consideration of the factor “time” transforms the 2D- or rather 3D- into a 4D-GIS based on a 4D geo-data infrastructure – the basis for a “time machine”. This enables the user to look at the forest at different (historical or future) conditions. The Virtual Forest serves as a foundation for abstracts of different economy units, which are from case to case both business and economically profitable, and with it for the management of larger amounts of wood at lower costs.

The ForestManager is an app that serves as a reminder in the management of forestland. Green areas and forests can be managed and documented with the support of GPS data. These functions offer the possibility to document observations, to take pictures, to print them, to send and to export them for further use. Moreover, it allows a comparison of historical and current data and can detect changes in the environment.

Technological assistance for the efficient management of forest areas.

Easily assess the sensitivity of forest soil to increased biomass harvesting. The owner or manager must enter soil characteristics into the digital or paper application, which indicates the sensitivity level for several mineral elements.

This tool allows more intensive forest management to be applied in areas where the risk of soil depletion is low. It is necessary to train forest owners to describe soil horizons.

This tool is complementary to the ADEME's guide "sustainable forest slash harvesting" of 2006 which indicates how to describe the soil (type of humus, soil texture, pH,...) and gives management recommendations according to the different types of sensitivity.
This application takes into account the pedoclimatic zone, humus type, pH, soil texture and prospective depth. The soil is described 25 cm deep. The result of the analysis gives 3 sensitivity levels: low, medium or high applied generally to the soil or for each mineral element (calcium, magnesium, potassium, phosphorus, nitrogen).