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Status: Confirmed |
Open to sharing: Yes |
Confidential: No |
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Transnational Access: Yes |
Open to training: Yes |
Grounded / Maintenance: No |
Aircraft name: Partenavia - IMAA
Airport: The area chosen for this study is the Bavarian Forest National Park which is more heterogeneous in tree species than similar areas in the region. It is located in south-eastern Germany along the border with the Czech Republic (490 3’ 19” N, 130 12’ 9” E). The park has a total area of 24,218 hectares. The study site is included in several European research projects and hands on data are available for the training course with the support of the “Data pool initiative for the Bohemian Forest Ecosystem”. Temporal airborne hyperspectral, LIDAR and aerial photography as well as high spatial resolution satellite images such as Rapid Eye, SPOT-5 and Sentinel2 acquired during the growing season of 2015 and 2016 in support of the RS4EBV project are available together with field measurements of plant traits for consecutive years.
Project theme: TA-013. Proposals for training courses in hyperspectral imaging applications or in-situ sampling
Project abstract: Forest management requires the use of comprehensive remote sensing data which enable monitoring biodiversity changes in response to calamities such as bark beetle infestation and other climate change induced phenomena. They also enable to predict the long-term impact of management decisions. Although the benefits of remote sensing for monitoring vegetation are well recognized, yet accurate and site specific monitoring of many essential biodiversity variables in forest ecosystems remain elusive. In this training course, the special skills required for processing the new generation of airborne and satellite hyperspectral, thermal and LIDAR data for retrieving essential biodiversity variables in forest ecosystems will be presented. In forests, bidirectional effects mainly influence hyperspectral airborne signals and directly affect the accuracy of derived variables. Simultaneous acquisition of thermal, VIS/NIR hyperspectral and LIDAR data (See RS4forestEBV-B) allow accurate retrieval of vegetation parameters (e.g., LAI, chlorophyll, SLA, nitrogen, water content, species occurrence and 3D vegetation structural attributes) which have been recognized as essential biodiversity variables by GEO-BON and are crucial in forestry and national park management practices. Several ongoing projects will support this training course including the ESA Innovator III project (RS4EBV). The participants will be trained in remote sensing algorithms and retrieval of essential biodiversity variables. The BIOKLIM project which is coordinated by Bavarian Forest National Park (BFNP), will provide data and expert knowledge on forest structure, biodiversity and management issues as well as facilitate access to the field sites, flux towers and field data collection techniques.
Measurements to be made by aircraft: Biophysical and biochemical vegetation parameters can characterize changes in biodiversity through changes in ecosystem structure and function. Although remote sensing, especially high spatial resolution hyperspectral imagery can be used to measure many biophysical and biochemical variables, retrieval of these parameters across different remote sensing systems to understand their dynamics remains an open challenge and the uncertainty sources and multiple approaches has to be taken in account. Commonly these data are used as stand-alone sources for retrieving the vegetation traits. However, the challenge is that in the presence of different data sources, which sources or combination of sources are most suitable for retrieving a specific variables and how these data sources can be used complementary. Therefore, two parallel proposals (RS4foestEBV- A and B) are suggested to address the aim of this training course which is to demonstrate how different remote sensing data and in-situ measurements of plant traits 1) can be used to model vegetation and 2) be linked to image data inversion in order to retrieve plant variables and map their spatial patterns. Accordingly, in this course the skills required for processing the new generation of airborne hyperspectral, thermal hyperspectral and LIDAR data for retrieving forest essential biodiversity traits will be presented. The course will highlight the added value of airborne data for forest management and biodiversity monitoring. Of special relevance is the envisaged test site (Bavarian Forest National Park) which encompasses a wide range of heterogeneous spatial patterns of temperate vegetation, where disturbances such as bark beetle calamities and storm damage substantially alter the structure of coniferous and mixed stands and cause changes to biodiversity. The simultaneous acquisitions of high spatial resolution airborne hyperspectral (See RS4forestEBV-B), thermal hyperspectral and LIDAR data allow us to better understand and monitor vegetation parameters. Furthermore, the ground data collection aims to provide the course participants with knowhow on tools (field spectroscopy, thermal spectrometry and terrestrial LIDAR) and measurement techniques to collect different vegetation variables. The training course enables the participants to achieve the following learning objectives: •To map different vegetation parameters using hyperspectral visible/NIR /thermal and LIDAR data ; •To understand the advantage of each data sources and the best combinations of them for retrieving vegetation parameters;. •To understand data processing chains; •To understand the challenge of collecting and integrating forest field data with remote sensing imagery; The training course will be structured as 4 stand-alone but interlinked working groups as described in the attachment. Although each working group will be piloted independently in the training course, they are all scientifically related and each forms an important component of the training course project. The overall themes of the working groups and their lead are as follows: •Working Group 1- Hyperspectral: to map the species occurrence, biophysical, biochemical properties and plant traits of the study area with high spectral resolution imagery by empirical and radiative transfer models; Lead by Roshanak Darvishzadeh; •Working Group 2- Thermal hyperspectral: to map the species occurrence, biophysical and biochemical properties of the study area with hyperspectral thermal imagery; Lead by Martin Schlerf; •Working Group 3- Atmospheric correction: to provide airborne reflectance comparable with proximal sensing including BRDF estimations and validation; Lead by Tiejun Wang •Working Group 4- LIDAR: to map the spatial patterns of heterogeneity and structural characteristics of the forest with laser scanner data; Lead by Marco Huerich All course participants (Working Groups) will be familiarized with the design of the flight and will be involved in sampling design and field measurements of the plant traits which will be used for analysis of the acquired images. They will all have the opportunity to learn tools and techniques used during vegetation field data collection. The University of Twente, Faculty of ITC, will be the coordinator and train participants in remote sensing algorithms for retrieval of essential biodiversity variables directly from acquired images. ITC has conducted several field measurements in BFNP and collected vegetation traits for the last three years. The BFNP as the key user has already established 330 sampling plots along four main straight transects covering the altitudinal and structural gradients. All environmental variables are derived from field measurements, aerial photographs, LIDAR data and climate stations. In 2016, the field measurements will be repeated in about half of these sample plots (i.e., 157) jointly with a LIDAR flight campaign covering gradients of altitude and forest structure. Moreover aerial photography is taking place every year in the park and the historical data are available. These huge source of existing data are an advantage for the training course, since the hands on data will be already accessible for the participants and can be used as reference dataset. Apart from participants of the training course, the collected data will be used by several PhD students who are studying the retrieval of different vegetation variables in the using different data sources at filed and airborne level. As there is an urgency to better characterize and understand forest ecosystem status in a time of rapid climate and landscape change, this training course offers EUFAR an opportunity to demonstrate innovative airborne science as well as having a valuable training component for PhD students and young carrier researchers.
Season: July 2017- Parallel to ICARE conference.
Weather constraints: The proposed activities require clear sky conditions (very low cloudiness and haze for hyperspectral sensors). Some (cumulus) clouds (up to 1/8) can be accepted.
Time constraints: The time schedule for the overflight requested in the present training course is the month of July in summer 2017 concurrent to ICARE conference which is hosted by DLR. Therefore, the ferry to the survey area will be minimized, since it is foreseen, that all aircraft will be all located at DLR during ICARE. Coincident times with the overpasses of Sentinel 2 are also desirable. The time of the flight should be as close as possible to local solar noon (i.e. from 10 AM to 2 PM local time for data acquisition) so as to minimize the effect of the anisotropy of the surface on reflectance measurements and under light winds to ensure stable flying conditions.
Flights (number and patterns): We propose one flight in July 2017 for approximately 10 hours with respect to the distance of the base APT.
Instruments: None
Other constraints: none
Name: SKIDMORE Andrew
PI email: a.k.skidmore@utwente.nl