Desmoid tumors of the abdominal wall: A case report

BACKGROUND: Desmoid tumors are slow growing deep fibromatoses with aggressive infiltration of adjacent tissue but without any metastatic potential. CASE PRESENTATION: We report on two female patients with desmoid tumor of the abdominal wall who underwent primary resection. Both patients had a history of an earlier abdominal surgery. Preoperative evaluation included abdominal ultrasound, magnetic resonance imaging and computed tomography. The histology in both cases revealed a desmoid tumor. CONCLUSION: Complete surgical resection is the first line management of this tumor entity

Vegetation Structure Modelling and Explorative Statistics based on Sentinel-1, Sentinel-2 and GEDI in the Paraguayan Chaco

Understanding vegetation structures in forests is crucial to estimate species composition, assess habitat quality, and preserve forest resources to ensure carbon storage and climate buffering. Tropical forests are the most endangered forest areas on Earth due to massive deforestation and degradation processes, although holding the richest biodiversity due to its complex vegetation structures. The Paraguayan Chaco is a subtropical dry forest that has experienced a total loss in forest area of about 30 % since the 1980s, resulting in a heavily fragmented landscape. To better understand and characterize the forest areas being lost and to preserve remaining forested areas, the present study implemented a workflow to model vegetation structure characteristics based on complementary satellite remote sensing data sets of Sentinel-1 (synthetic aperture radar), Sentinel-2 (multispectral), and the Global Ecosystem Dynamics Investigation (GEDI, Light Detection and Ranging, LiDAR). Since the Paraguayan Chaco comprises a study area of about 240 000 km², the processing of multi-temporal metrics of Sentinel was conducted in the cloud-computing platform Google Earth Engine (GEE). GEDI attributes of vegetation structure, such as canopy height, canopy cover density, and vertical foliage complexity, served as modelling responses in a Random Forest Regression model trained with comprehensive spatio-temporal metrics derived from Sentinel of 2019. The integration of novel GEDI samples (GEDI sensor is operating since April 2019) allows for large-scale extrapolation of vegetation structure characteristics. Therefore, the first high-resolution maps (10 m) of canopy height, total canopy cover, Plant-Area-Index, and Foliage-Height-Diversity-Index for 2019 have been generated for the Paraguayan Chaco. In addition, comprehensive statistics have been carried out, to better understand spatial patterns of vegetation structure and correlations of various environmental variables with modelled vegetation structure attributes. Distinct differences in seasonality and the expansion of agricultural fields are key influences that shape the vegetation in the Paraguayan Chaco that ranges from dense and high dry forest (maximum canopy height and total canopy cover: 17.6 m, 78.1 %) to grasslands and savannahs (maximum canopy height and total canopy cover: 1.8 m, 10 %). The canopy height model reached highest accuracy (R²: 64.0 %), followed by total canopy cover (R²: 61.4 %), Plant-Area-Index (R²: 50.6 %), and Foliage-Height-Diversity-Index (R²: 48.0 %). Explorative statistics show longitudinal gradients of vegetation structure with elevated values in the eastern part that is characterized by higher precipitation rates. In addition, the modelled characteristics of vegetation structure reflect the characteristics of an Ecoregion classification: Ecoregions with more fertile soils, higher precipitation rates, and weaker seasonality (Humid Chaco, Pantanal; eastern part of the Paraguayan Chaco) present higher, denser, and more complex forest structures than the Dry Chaco and Médanos (western and central part of the Paraguayan Chaco) which are Ecoregions that are strongly influenced by changes in temperature and precipitation due to changes in seasonality. The provision of continuous information of vegetation structure and a deeper understanding of environmental drivers will allow for a more comprehensive assessment of forest resources to support strategies for environmental-sound land use and prioritization of conservation areas in order to halt continuing deforestation activities in the Paraguayan Chaco

Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco

Abstract: Vegetation structure is a key component in assessing habitat quality for wildlife and carbon storage capacity of forests. Studies conducted at global scale demonstrate the increasing pressure of the agricultural frontier on tropical forest, endangering their continuity and biodiversity within. The Paraguayan Chaco has been identified as one of the regions with the highest rate of deforestation in South America. Uninterrupted deforestation activities over the last 30 years have resulted in the loss of 27% of its original cover. The present study focuses on the assessment of vegetation structure characteristics for the complete Paraguayan Chaco by fusing Sentinel-1, -2 and novel spaceborne Light Detection and Ranging (LiDAR) samples from the Global Ecosystem Dynamics Investigation (GEDI). The large study area (240,000 km²) calls for a workflow in the cloud computing environment of Google Earth Engine (GEE) which efficiently processes the multi-temporal and multi-sensor data sets for extrapolation in a tile-based random forest (RF) regression model. GEDI-derived attributes of vegetation structure are available since December 2019, opening novel research perspectives to assess vegetation structure composition in remote areas and at large-scale. Therefore, the combination of global mapping missions, such as Landsat and Sentinel, are predestined to be combined with GEDI data, in order to identify priority areas for nature conservation. Nevertheless, a comprehensive assessment of the vegetation structure of the Paraguayan Chaco has not been conducted yet. For that reason, the present methodology was developed to generate the first high-resolution maps (10 m) of canopy height, total canopy cover, Plant-Area-Index and Foliage-Height-Diversity-Index. The complex ecosystems of the Paraguayan Chaco ranging from arid to humid climates can be described by canopy height values from 1.8 to 17.6 m and canopy covers from sparse to dense (total canopy cover: 0 to 78.1%). Model accuracy according to median R² amounts to 64.0% for canopy height, 61.4% for total canopy cover, 50.6% for Plant-Area-Index and 48.0% for Foliage-Height-Diversity-Index. The generated maps of vegetation structure should promote environmental-sound land use and conservation strategies in the Paraguayan Chaco, to meet the challenges of expanding agricultural fields and increasing demand of cattle ranching products, which are dominant drivers of tropical forest los

Tropical Cyclone GIOVANNA. Madagascar, February 2012

JRC has developed GDACS, an early warning system created to alert the humanitarian community about potential disasters which are under development. Tropical cyclones are some of the most damaging events, affecting the coastal population with three dangerous effects: strong wind, heavy rain and storm surge. GDACS includes the analysis of the first and the second effects, and recently also the third effect (storm surge) has been implemented. An impact assessment for all the three alerts are presented in the report. Wind alert level estimated by GDACS was Red, due to the high wind and the high vulnerability of the affected country. The wind impact assessment by BNGRC has confirmed that most of the damage due to Giovanna was caused by strong winds. The region most affected has been Antisanana. The rain impact alert level in GDACS is based on the estimation of the total accumulation of rainfall on land using NOAA eTRaP data. The applicability of the data was considered fine for alert levels at regional level, but not at local level due to spatial uncertainty. The storm surge GDACS alert level is based on the calculations of the JRC code HyFlux2. The accuracy of the estimated storm surge height could not be established because the available tide gauge was malfunctioning. We compared our results with two UNOSAT/UNITAR impact assessment maps of two damaged cities (Brickaville and Vatomadry). These maps gave a clear indication of building damages, as a result of strong winds and storm surge while the JRC calculations showed a storm surge in the order of 1 m. Overall, the GDACS models performed well. Alert levels for all hazard components were consistent with the observed impact. The location and timing of the information could accurately identify the affected provinces. GDACS information is appropriate for near real-time strategic decision making.JRC.G.2-Global security and crisis managemen

Interoperability of Mobile Devices for Crisis Management: Outcomes of the 1st JRC ECML Crisis Technology Workshop

The 1st JRC ECML Crisis Technology Workshop on Mobile Interoperability for International Field Deployment took place in the European Crisis Management Laboratory (ECML) of the Joint Research Centre in Ispra, Italy, from 12 to 13 March 2012. 37 participants attended the workshop. They were coming from: 11 EU countries and Norway, Brazil and US, 3 UN agencies, and 2 NGOs. The workshop's purpose was to measure the added value of mobile assessment technology for rapid situation assessment in international emergency operations. Seven mobile assessment systems were deployed among the participants and needed to provide, in an interoperable way, real-time data to a single electronic On-Site Operations Coordination Centre (eOSOCC). The performance of the systems was benchmarked against a traditional paper-based assessment that was conducted simultaneously (pOSOCC). In the workshop experiment both paper and electronic On-Site Operations Coordination Centres (OSOCCs) reached a similar situation awareness in the same time, but only the eOSOCC had products available as sharable electronic maps and documents. The final map with all incoming feeds in the eOSOCC was very cluttered and there was considerable information overload. Therefore sophisticated editing, filtering, and visualization functionalities have to be available for eOSOCC staff. Mobile technology is mature and can be deployed in an interoperable way. However, then the information of each system leaves the proprietary applications for processing and analyzing the data. The main impression from the eOSOCC team was that there is a lot of potential. Having access in real-time to field information was felt to be extremely useful. Still missing are tools and procedures for exploiting this benefit. Most important are tools to curate, filter, manipulate, edit, and delete assessment information of all teams. A dedicated eOSOCC software suite is needed that gives full control over the data to the eOSOCC staff.JRC.G.2-Global security and crisis managemen

A surface water body dataset with daily temporal resolution: Selected examples and application potential of the Global WaterPack

Information on the distribution of inland waters bodies and their seasonal variability is important for supporting water and land management and informed decision making. Furthermore, such data can be crucial basis for scientific analyses in the field of regional and global environmental research. Key functions and services of wetlands for example are closely related to the temporal variations of surface water availability and inundation cycles. Over the past years the mapping of temporal water dynamics from earth observation data has received increasing attention. Open data policies as well as the progress in computing power and processing techniques have played a major role for this development. In the past decades there has been several optical instruments available collecting data on global scale with very high temporal resolution, such as MODIS or SPOT-VGT/Proba-V. With the launch of Sentinel-3 and Suomi-NPP such observations at global scale and high temporal resolution has been secured for the near future. Recently, many studies already underlined the relevance of mapping water with high temporal resolution presenting essential results and interesting findings. In our research we are presenting DLRs Global WaterPack, a MODIS-based 250m time series dataset of surface water dynamics with daily temporal resolution. Using examples of lakes and reservoirs from around the Earth, the potential of the Global WaterPack to capture relevant parameters such as inundation frequency and duration, timing of flooding and water retreat, as well as freezing and thawing cycles, is presented. Results are compared with high resolution spatial reference data and in-situ measurements. The application potential of the Global WaterPack time series for monitoring and assessing changes is discussed. Furthermore, we discuss this potential based on selected examples which underline the added value of water surface detection at high temporal resolution in the context of climate and environmental change. Short and long-term dynamics of lakes and reservoirs and the underlying climatic or anthropogenic processes often cannot be determined in detail in regards to time by assessing interrupted time series or multi-temporal watermask snapshot observations. The entire MODIS time series from 06/2002-today is currently being processed and the dataset will be tested as input for global hydrological models. Additionally, quantification of uncertainty is being planned and will be part of future development

AVHRR NDVI Compositing Method Comparison and Generation of Multi-decadal Time Series —A TIMELINE Thematic Processor

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument provides daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change related environmental processes. For monitoring vegetation condition, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as there is no standard procedure established. 13 different compositing methods have been implemented, daily, decadal and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best performing compositing algorithm considering all investigated aspects. However, also the combination of NDVI value and viewing and illumination angles as criteria for best-pixel selection proved to be a promising approach. The generated NDVI time series, currently ranging from 1981 - 2018, shows a consistent behavior and a close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites

Breaking new ground in mapping human settlements from space -The Global Urban Footprint-

Today 7.2 billion people inhabit the Earth and by 2050 this number will have risen to around nine billion, of which about 70 percent will be living in cities. Hence, it is essential to understand drivers, dynamics, and impacts of the human settlements development. A key component in this context is the availability of an up-to-date and spatially consistent map of the location and distribution of human settlements. It is here that the Global Urban Footprint (GUF) raster map can make a valuable contribution. The new global GUF binary settlement mask shows a so far unprecedented spatial resolution of 0.4 arcsec ($\sim12 m$) that provides - for the first time - a complete picture of the entirety of urban and rural settlements. The GUF has been derived by means of a fully automated processing framework - the Urban Footprint Processor (UFP) - that was used to analyze a global coverage of more than 180,000 TanDEM-X and TerraSAR-X radar images with 3m ground resolution collected in 2011-2012. Various quality assessment studies to determine the absolute GUF accuracy based on ground truth data on the one hand and the relative accuracies compared to established settlements maps on the other hand, clearly indicate the added value of the new global GUF layer, in particular with respect to the representation of rural settlement patterns. Generally, the GUF layer achieves an overall absolute accuracy of about 85\%, with observed minima around 65\% and maxima around 98 \%. The GUF will be provided open and free for any scientific use in the full resolution and for any non-profit (but also non-scientific) use in a generalized version of 2.8 arcsec ($\sim84m$). Therewith, the new GUF layer can be expected to break new ground with respect to the analysis of global urbanization and peri-urbanization patterns, population estimation or vulnerability assessment

Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product

Remote sensing multi-decadal time-series provide important information for analysing long-term environmental change. The Advanced Very High Resolution Radiometer (AVHRR) has been providing data since the early 1980s. Normalised Difference Vegetation Index (NDVI) timeseries derived thereof can be used for monitoring vegetation conditions. This study presents the novel TIMELINE NDVI product, which provides a consistent set of daily, 10-day, and monthly NDVI composites at a 1 km spatial resolution based on AVHRR data for Europe and North Africa, currently spanning the period from 1981 to 2018. After investigating temporal and spatial data availability within the TIMELINE monthly NDVI composite product, seasonal NDVI trends have been derived thereof for the period 1989–2018 to assess long-term vegetation change in Europe and northern Africa. The trend analysis reveals distinct patterns with varying NDVI trends for spring, summer and autumn for different regions in Europe. Integrating the entire growing season, the result shows positive NDVI trends for large areas within Europe that confirm and reinforce previous research. The analyses show that the TIMELINE NDVI product allows long-term vegetation dynamics to be monitored at 1 km resolution on a pan-European scale and the detection of specific regional and seasonal patterns

Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Encroachment Mapping in the Free State Province, South Africa

Increasing woody cover and overgrazing in semi arid ecosystems are known to be major factors driving land degradation. During the last decades woody cover encroachment has increased over large areas in southern Africa inducing environmental, land cover as well as land use changes. The goal of this study is to synergistically combine SAR (Sentinel 1) and optical (Sentinel 2) earth observation information to monitor the slangbos encroachment on arable land in the Free State province, South Africa, between 2015 and 2020. Both, optical and radar satellite data are sensitive to different land surface and vegetation properties caused by sensor specific scattering or reflection mechanisms they rely on. This study focuses on mapping the slangbos aka bankrupt bush (Seriphium plumosum) encroachment in a selected test region in the Free State province of South Africa. Though being indigenous to South Africa, the slangbos has been documented to be the main encroacher on the grassvelds (South African grassland biomes) and thrive in poorly maintained cultivated lands. The shrub reaches a height and diameter of up to 0.6 m and the root system reaches a depth of up to 1.8 m. Slangbos has small light green leaves unpalatable to grazers due to their high oil content and is better adapted to long dry periods compared to grass communities. We used the random forest approach to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were based on expert knowledge and field information from the Department of Agriculture, Forestry and Fisheries (DAFF). Several input variables have been tested according to their model performance, e.g. backscatter, backscatter ratio, interferometric coherence as well as optical indices (e.g. NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), EVI (Enhanced Vegetation Index), etc.). We found that the Sentine 1 VH backscatter (vertical horizontal/cross polarization) and the Sentinel 2 SAVI time series information have the highest importance for the random forest classifier among all input parameters. The estimation of the model accuracy was accomplished via spatial cross validation and resulted in an overall accuracy of above 80 % for each time step, with the slangbos class being close to or above 90 %. Currently we are developing a prototype application to be tested in cooperation with local stakeholders to bring this approach to the farmers level. Once field work in southern Africa is possible again, further ground truthing and interaction with farmers will be carried out