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Agency: European Commission | Branch: FP7 | Program: CP-FP-SICA | Phase: SPA.2010.3.2-02 | Award Amount: 616.11K | Year: 2011

The REDD Fast Logging Assessment & Monitoring Environment (REDD-FLAME) project will design, prototype and demonstrate a system capable of monitoring tropical and sub-tropical forests using high-resolution radar (and optical) imagery acquired by Earth Observation satellites. By focussing on early detection of logging activities, the system will provide the means to quickly identify the first signs of deforestation and thus act as a tool to control resource use and sustainable development within these fragile and valuable environments. The system will form a high-resolution add-on for existing (semi-)operational low- to mid-resolution systems, providing hot-spot monitoring for areas at highest risk of deforestation. As such, it could be integrated into national or regional forest monitoring centres and provide inputs for large scale carbon emission assessments in the context of the UN-REDD (Reducing Emissions from Deforestation and Forest Degradation) Programme. The system will be developed in collaboration with investigators from developing countries on three continents in an effort to build lasting partnerships and transfer European expertise. Sites in Indonesia, Brazil and Mozambique have been chosen to represent a variety of forest types and deforestation issues, and thus to test the systems versatility. The REDD-FLAME project will bring benefits to a wide range of parties concerned with deforestation in tropical and sub-tropical environments and build capacity in the host countries for managing forest resources and carbon balances. By implementing a dedicated version of REDD-FLAME for each participating ICP country, results targeted at their specific monitoring requirements, based on the strategies and policies for deforestation in place, will be provided to give timely information on logging activities and deforestation.

Agency: European Commission | Branch: FP7 | Program: CP-FP-SICA | Phase: SPA.2010.3.2-03 | Award Amount: 585.48K | Year: 2011

In Africa today, malaria is understood to be both a disease of poverty and a cause of poverty. Nevertheless, malaria is a preventable and curable disease. According to the Abuja declaration, the goal is to halve malaria mortality in Africa by 2010. (WHO, 2008, Status Report on Malaria in the African region). This report also states that most of the African countries within the region are attempting universal access to malaria prevention and control in the at risk areas. Fundamental is the control and management of the vector. There is a need for accurate and effective monitoring and evaluation systems as a resource to inform decisions that will assist in achieving these goals. Partnership between and within countries are essential to assist with access to and sharing of resources. The primary objective of MALAREO is to develop technology and implement EO capacities within malaria vector control and management programs in southern Africa. To achieve this objective, knowledge exchange and capability will occur in two directions (EU <-> SA). By doing this, the project will contribute to the installation of an EO monitoring cell that will support the daily work of the national malaria control programs.

Agency: European Commission | Branch: FP7 | Program: CSA-CA | Phase: ENERGY-2007-3.7-01;ENERGY-2007-7.3-01 | Award Amount: 1.34M | Year: 2008

The main objective of the project is to develop a common methodology for gathering information on biomass potential using terrestrial and earth observations. This objective will be achieved by the implementation of a systematic assessment work plan and will result in the establishment of a harmonised approach and an e-training tool for dissemination. The e-training environment will be an important tool for reaching the much-needed European harmonisation, whereas a Stakeholder Platform will facilitate access to reliable and common datasets on biomass potential and as such it will offer a more efficient use of the available European biomass feedstock. The project will: - Develop a common methodology for gathering information on biomass potential using terrestrial and earth observations - Use e-technologies for disseminating information, best practices on the use and applicability of developed harmonised methodology

News Article | December 1, 2016
Site: astrobiology.com

If you think operating a robot in space is hard, try doing it in the ocean. Saltwater can corrode your robot and block its radio signals. Kelp forests can tangle it up, and you might not get it back. Sharks will even try to take bites out of its wings. The ocean is basically a big obstacle course of robot death. Despite this, robotic submersibles have become critical tools for ocean research. While satellites can study the ocean surface, their signals can't penetrate the water. A better way to study what's below is to look beneath yourself -- or send a robot in your place. That's why a team of researchers from NASA and other institutions recently visited choppy waters in Monterey Bay, California. Their ongoing research is developing artificial intelligence for submersibles, helping them track signs of life below the waves. Doing so won't just benefit our understanding of Earth's marine environments; the team hopes this artificial intelligence will someday be used to explore the icy oceans believed to exist on moons like Europa. If confirmed, these oceans are thought to be some of the most likely places to host life in the outer solar system. A fleet of six coordinated drones was used to study Monterey Bay. The fleet roved for miles seeking out changes in temperature and salinity. To plot their routes, forecasts of these ocean features were sent to the drones from shore. The drones also sensed how the ocean actively changed around them. A major goal for the research team is to develop artificial intelligence that seamlessly integrates both kinds of data. "Autonomous drones are important for ocean research, but today's drones don't make decisions on the fly," said Steve Chien, one of the research team's members. Chien leads the Artificial Intelligence Group at NASA's Jet Propulsion Laboratory, Pasadena, California. "In order to study unpredictable ocean phenomena, we need to develop submersibles that can navigate and make decisions on their own, and in real-time. Doing so would help us understand our own oceans -- and maybe those on other planets." Other research members hail from Caltech in Pasadena; the Monterey Bay Aquarium Research Institute, Moss Landing, California; Woods Hole Oceanographic Institute, Woods Hole, Massachusetts; and Remote Sensing Solutions, Barnstable, Massachusetts. If successful, this project could lead to submersibles that can plot their own course as they go, based on what they detect in the water around them. That could change how we collect data, while also developing the kind of autonomy needed for planetary exploration, said Andrew Thompson, assistant professor of environmental science and engineering at Caltech. "Our goal is to remove the human effort from the day-to-day piloting of these robots and focus that time on analyzing the data collected," Thompson said. "We want to give these submersibles the freedom and ability to collect useful information without putting a hand in to correct them." At the smallest levels, marine life exists as "biocommunities." Nutrients in the water are needed to support plankton; small fish follow the plankton; big fish follow them. Find the nutrients, and you can follow the breadcrumb trail to other marine life. But that's easier said than done. Those nutrients are swept around by ocean currents, and can change direction suddenly. Life under the sea is constantly shifting in every direction, and at varying scales of size. "It's all three dimensions plus time," Chien said about the challenges of tracking ocean features. "Phenomena like algal blooms are hundreds of kilometers across. But small things like dinoflagellate clouds are just dozens of meters across." It might be easy for a fish to track these features, but it's nearly impossible for an unintelligent robot. "Truly autonomous fleets of robots have been a holy grail in oceanography for decades," Thompson said. "Bringing JPL's exploration and AI experience to this problem should allow us to lay the groundwork for carrying out similar activities in more challenging regions, like Earth's polar regions and even oceans on other planets." The recent field work at Monterey Bay was funded by JPL and Caltech's Keck Institute for Space Studies (KISS). Additional research is planned in the spring of 2007. Caltech in Pasadena, California, manages JPL for NASA. For more information about this research, visit: http://kiss.caltech.edu/new_website/techdev/seafloor/seafloor.html

Agency: European Commission | Branch: H2020 | Program: RIA | Phase: SC5-16-2014 | Award Amount: 4.98M | Year: 2015

The objective of the project SWOS is to develop a monitoring and information service focussing on wetland ecosystems. Globally wetlands are the ecosystems with the highest rate of loss. This is alarming, considering their significance as biodiversity hotspots and ecosystems with a central role in the water cycle, including improving water quality and reducing water scarcity, in climate regulation and the economic benefit gained from using their services. A key limitation to their more effective conservation, sustainable management and restoration is the missing knowledge underpinning the application of European policy by Member States. Under the Biodiversity Strategy, Member States have recently committed to the mapping and assessment of ecosystem services (MAES); this provides a key instrument for an improved integration of wetlands in policy. SWOS will take full advantage of the Sentinel satellites and integrate results from the ESA Globwetland projects. Status maps and indicators, as well as near real-time observations will allow the assessment of biodiversity and the monitoring of dynamic changes in an unmatched temporal and spatial resolution. The Service Portal will allow the integration and web-based analysis of new maps and in-situ measurements and provide a unique entry point to locate, access and connect existing information and databases. It follows a GEOSS compatible data-broker approach and adopts international standards. SWOS contributes to establishing a Global Wetland Observing System, as requested by Ramsar, it will facilitate local and EU monitoring tasks and input into international reporting obligations. SWOS will position Europe in a leading role for wetland activities within the GEO ecosystem, biodiversity, water, land cover tasks. The direct involvement of users working at different scales and support of key user organizations ensures the usability and acceptance of the service, the harmonization with related activities and a long-term impact.

Wang X.,Wuhan University | Wang X.,Ludwig Maximilians University of Munich | Siegert F.,Ludwig Maximilians University of Munich | Siegert F.,Remote Sensing Solutions GmbH | And 2 more authors.
Global and Planetary Change | Year: 2013

The alpine ecosystem of the Western Nyainqentanglha region, located in the Central Tibetan Plateau, has experienced a lot of changes in the context of climatic change. The long data record of remote sensing data allowed us to evaluate spatio-temporal change in this remote area. The ecosystem changes of the Western Nyainqentanglha region were detected by using Landast MSS/TM/ETM+, Hexagon KH-9, Glas/ICESat, SRTM3 DEM remote sensing data and GIS techniques. The area of glacier lakes was delineated by visual interpretation, while for the inland lake by image classification. The change of glacier thickness was obtained by Glas/ICESat data of 2004 and 2008. Results show high variation in extent of glaciers and lakes with increased temperature and precipitation in the past 40years. These variations include glacial retreat, increased water level of inland lakes and increased number of glacier lakes to higher altitudes. Glaciers lost 22% of its coverage from 1977 to 2010, and the annual shrinkage rate accelerated in the last decade compared with the previous time period of 1977-2001. In average, the thickness of the monitored glaciers reduced by 4.48m from 2004 to 2008 with an annual rate of 1.12m. From 1972 to 2009, the number of new formed glacier lakes increased by 150 and the area of glacier lakes increased by 173% (4.53km2). At the same time, the surface area of the largest salt lake in Tibet expanded by 4.13% (80.18km2). These variations appear to be associated with an increase in mean annual temperature of 0.05°C per year, and an increase in annual precipitation of 1.83mm per year in the last four decades. By analyzing the relationship between the decreased glacier area and the increased number and extent of lakes in the vertical zones over the past 40years, there is a high correlation of 0.81. These results indicate that the climate change has great impacts on glaciers and glacier lakes on the central Tibetan Plateau. Further detailed investigations are required to understand the contribution of melting water and precipitation to the water cycle and the complicated hydrological relationship between the characteristics of glaciers and glacier lakes and climate warming in this alpine region. © 2013 Elsevier B.V.

Englhart S.,Ludwig Maximilians University of Munich | Keuck V.,Remote Sensing Solutions GmbH | Siegert F.,Ludwig Maximilians University of Munich | Siegert F.,Remote Sensing Solutions GmbH
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2012

In the context of climate change mitigation mechanisms for avoiding deforestation, i.e., reducing emissions from deforestation and forest degradation (REDD+), comprehensive forest monitoring, especially in tropical regions, is of high relevance. A precise determination of forest carbon stocks or aboveground biomass (AGB) for large areas is of special importance. This study analyzes and compares three different methods for retrieving AGB in Indonesia's peat swamp forests from multi-frequency SAR backscatter data. Field inventory AGB data were related to LiDAR measurements allowing plentiful accurate AGB estimations. These estimatedAGB data provided a powerful basis for SAR based AGB model calibration and validation. Multivariate linear regression (MLR), artificial neural network (ANN) and support vector regression (SVR) were examined for their performance to retrieve AGB on the basis of multi-temporal TerraSAR-X and ALOS PALSAR imagery. The MLR model resulted in lower coefficients of determination and higher error measures than the other two approaches and showed significant overestimations in the high biomass range. The SVR modeled AGB was more accurate than ANN modeled AGB in terms of independent validation, but showed less variation in the spatial distribution of AGB and saturated at approximately 260 t/ha. The ANN model showed a superior performance for modeling AGB up to 650 t/ha without a saturation in the lower biomass ranges. For the needs of REDD+, it is very important to know the possibilities, constraints and uncertainties of AGB retrieval based on satellite imagery. © 2011 IEEE.

Jubanski J.,Remote Sensing Solutions GmbH | Ballhorn U.,Remote Sensing Solutions GmbH | Kronseder K.,Remote Sensing Solutions GmbH | Franke J.,Remote Sensing Solutions GmbH | And 2 more authors.
Biogeosciences | Year: 2013

Quantification of tropical forest above-ground biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne light detection and ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed, and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density per square metre of about 4 resulted in the best cost / benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site-specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 43%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong greenhouse gas (GHG) emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments. © Author(s) 2013.

Englhart S.,Ludwig Maximilians University of Munich | Keuck V.,Remote Sensing Solutions GmbH | Siegert F.,Ludwig Maximilians University of Munich | Siegert F.,Remote Sensing Solutions GmbH
Remote Sensing of Environment | Year: 2011

In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (> 100. t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was r2 = 0.53 with an RMSE of 79. t/ha. The model is valid up to 307. t/ha with an accuracy requirement of 50. t/ha and up to 614. t/ha with an accuracy requirement of 100. t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types. © 2011 Elsevier Inc.

Englhart S.,Ludwig Maximilians University of Munich | Englhart S.,Remote Sensing Solutions GmbH | Jubanski J.,Remote Sensing Solutions GmbH | Siegert F.,Ludwig Maximilians University of Munich | Siegert F.,Remote Sensing Solutions GmbH
Remote Sensing | Year: 2013

Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height histograms for 2007 (r2 = 0.77, n = 79) and 2011 (r2 = 0.81, n = 53), taking the different point densities into account. Changes in peat swamp forests were identified by analyzing multispectral imagery. Unaffected forests accumulated on average 20 t/ha AGB with a canopy height increase of 2.3 m over the four year time period. Selective logged forests experienced an average AGB loss of 55 t/ha within 30 m and 42 t/ha within 50 m of detected logging trails, although the mean canopy height increased by 0.5 m and 1.0 m, respectively. Burned forests lost 92% of the initial biomass. These results demonstrate the great potential of repetitive airborne LiDAR surveys to precisely quantify even small scale AGB and canopy height dynamics in remote tropical forests, thereby featuring the needs of REDD+. © 2013 by the authors.

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