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Sitzungsübersicht
Sitzung
S1.1: Forst
Zeit:
Donnerstag, 05.03.2020:
10:30 - 12:00

Chair der Sitzung: Peter Krzystek
Chair der Sitzung: Georg Bareth
Ort: Raum 114
Gem. Sitzung Forst und Agrar mit Multi-Hyperspektral

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Präsentationen

Assessment of drought effects on forests using non-parametric methods and satellite imagery

S. König1, J. Schultz1,2, O. Dubovyk1,3, F. Thonfeld4,5

1Geographisches Institut, Universität Bonn; 2Geographisches Institut, Ruhr-Universität Bochum; 3Zentrum für Fernerkundung der Landoberfläche, Universität Bonn; 4Deutsches Fernerkundungsdatenzentrum; 5Institut für Geographie und Geologie, Universität Würzburg

Forests are an essential part of both the natural environment and human existence. Locally and regionally, they provide a variety of ecosystem services, including wood related products, the filtering of water, climate regulatory functions and the potential for recreation and tourism. Forestry is among the most important economic sectors in Europe, especially in Germany. Forests are biodiversity hosts, a key part of global land-atmosphere interactions and a major net carbon sink. The amount of carbon stored in temperate forests increased by 17% in 2000-2007 compared to 1990-1999, contrasting tropical and boreal forests and partly outweighing anthropogenic emissions. Hence, temperate forests play a key role in mitigating climate change.

Natural disturbances, such as windthrow, fire and insect outbreaks, are vital for ecosystem function and dynamics of forests. They change forest's composition, structure and function and alter the resources available to the organisms in it. Consequently, disturbances can support landscape heterogeneity, increase biodiversity and benefit succession and renewal throughout the ecosystem. Disturbance regimes in forests, however, are changing globally, with global warming being a key driver. With increasing climatic variability and ongoing climate change, the frequency and intensity of droughts has increased recently. This has profound implications also on the forest ecosystems in Germany and results in increased tree mortality. Droughts affect forests directly by increasing evaporation and heat stress during summer and decreasing the share of snow in total precipitation that otherwise would act as a moisture reservoir. Indirectly, droughts increase temperate forest's vulnerability to other disturbance agents, especially insect outbreaks, pathogens and wildfire while at the same time facilitating their activity.

Consequently, it is crucial to study drought's effects on temperate forests and monitor their response operationally. Here, we present a novel remote sensing-based approach that compares the phenological characteristics of forests in drought affected years to their typical pattern of vegetation development of a reference period. We used the severe drought that affected major parts of Central Europe in 2018 as an example for this study. This drought was accompanied by both exceedingly low precipitation as well as high temperatures for the months of April to August. While this drought was not uniform over all of Europe, Germany was largely affected, with temperatures being exceeded only by conditions of the major heatwave of 2003 and a strong rainfall decrease especially in the country's North and central East. Consequently, our approach was tested for two areas in Germany - one in the south of North Rhine Westphalia in the country's western part and one in Brandenburg in its east, both being characterized by mixed forest, but experiencing somewhat different precipitation patterns.

For both areas, all available Landsat and Sentinel-2 images from January 2000 until September 2019 were acquired. Data was atmospherically corrected and stacked into one consistent data cube of Normalized Difference Vegetation Index (NDVI) values with a resolution of 30 m. Processing was performed using the software Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) and included a removal of cloud- or snow-covered as well as otherwise unsuitable pixels. Using Google Earth Engine, we created a mask of all pixels that did not experience any stand-replacing disturbance event between 2000 and 2017 by utilizing the Global Forest Change Dataset. After masking all areas that did exhibit such a disturbance event, each remaining pixel's time series was divided into two parts. All observations between 2000 and 2017 were used as the reference of vegetation development and the years 2018 and 2019 constituted the monitoring period. 2019 was included to also observe ongoing drought effects during the vegetation period after the event.

For reference period of every pixel, a 2-dimensional kernel density estimation was made, with day of the year (DOY) as the and NDVI value as the variable. By standardization to sum 1, this density estimation is transferred into a probability distribution. The most likely NDVI value per DOY is then used as the baseline phenological cycle. This procedure has several advantages. First, this is a non-parametric approach that does not rely on a predefined function to describe vegetation development throughout the year and that doesn’t require any calibration. Second, it enables the estimation of an observation in the monitoring period being an anomaly. This is achieved by comparing its NDVI value to the probability distribution.

For both areas, NDVI values of 2018 and 2019 were compared to the estimated benchmark of vegetation development. While the difference in NDVI between the modelled baseline and the reference period shows some variability and faulty values, there is a detectable pattern. NDVI values where lower in 2018 compared to the phenological baseline in most areas, but the probability of these of being an anomaly was often low (less than 0.5). This suggests that values where not low beyond historical record and it is likely that this NDVI decrease can be attributed to natural variability, especially because observations were typically in line with or higher than past values in 2019. For some smaller areas however, NDVI values were exceptionally low in 2018, followed by consistently very low rates in 2019. This indicates that trees in this area were possibly impacted by drought or another disturbance agent that may interact with it such as bark beetles, causing die-off of trees.

While the approach presented here appears useful to detect the impact of droughts on forest areas and this study serves as a proof of concept, there are some aspects that should be further considered. First, the procedure was originally developed for MODIS Enhanced Vegetation Index (EVI) data, and transferring it to a combined Landsat-Sentinel-2 NDVI time series may require some additional adjustments such as time series smoothing to remove some of the variability mentioned above. Second, the use of other remotely sensed indices such as the Normalized Difference Moisture Index (NDMI) or the vegetation Condition Index (VCI) that may capture the impact of droughts on trees better may result in more accurate results. Third, while disturbance impacts can be quantified, we cannot yet infer the actual disturbance agent from the data using this approach. Since this study considered a drought year, drought is possibly a major influence, yet it is not clear whether the NDVI response, especially in case of detected mortality, is caused by water stress directly or rather by a subtler drought impact or a different driver. Still, it exhibits some promising results that should further be elaborated and tested in different locations to gain more insight into drought impacts.



Case study: Near real-time thermal mapping to support firefighting and crisis management

T. Bucher

DLR - Deutsches Zentrum für Luft- und Raumfahrt e.V., Deutschland

Hot and dry summers have led to an increase in forest fires both concerning numbers and intensity in north-eastern Germany in the last years. In the project FireSense the German Aerospace Center (DLR) has adapted its sensor system MACS (Modular Airborne Camera System) with a set of thermal mid- and long wave infrared (MWIR and LWIR) cameras to detect, monitor and quantify high temperature events (HTE) like forest fires. Ground-based, airborne and spaceborne measurements over fire-experiments are synchronized for cross-validation of the systems and to test the developed workflows.

In summer 2019 gas flaring tests were conducted in cooperation of DLR and the Federal Institute for Material Research and Testing (BAM), parallel several large forest fires in Brandenburg (Lieberose) and Mecklenburg-Vorpommern (Lübtheen) developed. In coordination with the crisis management group (local authorities, firefighters, armed forces, federal police) to get the permits MACS conducted 3 flights over the fires in altitudes between 6000 (sunny) down to 3500 ft (under clouds), Lübtheen was covered twice, on July 2 and July 4, when the fire was already under control. Synchronously firefighting helicopters operated close to ground, also delivering videos of the fires for visual interpretation.

To get both background temperatures for orientation and landscape features and also information about the fires within one data set, a broad calibration range for the LWIR camera was commanded. Using synchronized position- and orientation data of MACS with given calibration data and a Digital Terrain Model, direct geocoding and the processing of near real-time mosaics was possible using the DLR workflow even without post-processing. The accuracy was sufficient for planning purposes. Geo-tiff maps were delivered shortly after landing within less than three hours. The real-time capabilities of the system could not be used as the flights were conducted on very short notice and the radio link was not installed.

The thermal data were delivered as false color heat maps. They show the thermal anomalies very well, clearly discriminating burning area, recently burnt area and unaffected forest. In the RGB data the ground fires are rarely visible as they are covered by and almost did not affect the closely standing crowns. The spread of the fires can be seen in the overlapping regions of adjacent flight lines.

Data exchange and use of the data proved to be difficult due to limited data rates and IT infrastructure in the command and situation center in the field, sometimes taking more time than the acquisition and processing. This reduces the practical benefit for the data in the field. For future planned experiments for real-time mapping of forest fires this will be one of the main points to improve the latency of the data transfer to the control center ideally by using a live data link and to optimize the coordination with the control center. Further activities will be coordinated by the Helmholtz Innovation Lab OPTSAL (Optische Technologien für Situationserfassung im Sicherheitsbereich), which was started at DLR in 2020. In OPTSAL hard- and software solutions are developed and activities concerning situational awareness for safety and security are coordinated with industry and authorities.



Synergetic use of Planet data and high-resolution aerial images for windthrow detection based on Deep Learning

W. Deigele1,2, M. Brandmeier1, Z. Hamdi1,2, C. Straub3

1Esri Deutschland GmbH, Deutschland; 2Technische Universität München; 3LWF Freising

Due to climate change the number of storms and, thus, forest damage has increased over recent years. The state of the art of damage detection is manual digitization based on aerial images and requires a great amount of work and time. There have been numerous attempts to automatize this process in the past such as change detection based on SAR and optical data or the comparison of Digital Surface Models (DSMs) to detect changes in the mean forest height. By using Convolutional Neural Networks (CNNs) in conjunction with GIS we aim at completely streamlining the detection and mapping process.

We developed and tested different CNNs for rapid windthrow detection based on Planet data that is rapidly available after a storm event, and on airborne data to increase accuracy after this first assessment. The study area is in Bavaria (ca. 165 square km) and data was provided by the agency for forestry (LWF). A U-Net architecture was compared to other approaches using transfer learning (e.g. VGG32) to find the most performant architecture for the task on both datasets. U-Net was originally developed for medical image segmentation and has proven to be very powerful for other classification tasks.

Preliminary results highlight the potential of Deep Learning algorithms to detect damaged areas with accuracies of over 91% on airborne data and 92% on Planet data. The proposed workflow with complete integration into ArcGIS is well-suited for rapid first assessments after a storm event that allows for better planning of the flight campaign, and first management tasks followed by detailed mapping in a second stage.



 
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