37,819 research outputs found

    Building capacity in remote sensing for conservation: present and future challenges

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    Remote sensing (RS) has made significant contributions to conservation and ecology; however, direct use of RS-based information for conservation decision making is currently very limited. In this paper, we discuss the reasons and challenges associated with using RS technology by conservationists and suggest how training in RS for conservationists can be improved. We present the results from a survey organized by the Conservation Remote Sensing Network to understand the RS expertise and training needs of various categories of professionals involved in conservation research and implementation. The results of the survey highlight the main gaps and priorities in the current RS data and technology among conservation practitioners from academia, institutions, NGOs and industry. We suggest training to be focused around conservation questions that can be addressed using RS-derived information rather than training pure RS methods which are beyond the interest of conservation practitioners. We highlight the importance of developing essential biodiversity variables (EBVs) and how this can be achieved by increasing the RS capacity of the conservation community. Moreover, we suggest that open-source software is adopted more widely in the training modules to facilitate access to RS data and products in developing countries, and that online platforms providing mapping tools should also be more widely distributed. We believe that improved RS capacity among conservation scientists will be essential to improve conservation efforts on the ground and will make the conservation community a key player in the definition of future RS-based products that serve conservation and ecological needs

    Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon

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    Peru has the fourth largest area of peatlands in the Tropics. Its most representative land cover on peat is a Mauritia flexuosa dominated palm swamp (thereafter called dense PS), which has been under human pressure over decades due to the high demand for the M. flexuosa fruit often collected by cutting down the entire palm. Degradation of these carbon dense forests can substantially affect emissions of greenhouse gases and contribute to climate change. The first objective of this research was to assess the impact of dense PS degradation on forest structure and biomass carbon stocks. The second one was to explore the potential of mapping the distribution of dense PS with different degradation levels using remote sensing data and methods. Biomass stocks were measured in 0.25 ha plots established in areas of dense PS with low (n = 2 plots), medium (n = 2) and high degradation (n = 4). We combined field and remote sensing data from the satellites Landsat TM and ALOS/PALSAR to discriminate between areas typifying dense PS with low, medium and high degradation and terra firme, restinga and mixed PS (not M. flexuosa dominated) forests. For this we used a Random Forest machine learning classification algorithm. Results suggest a shift in forest composition from palm to woody tree dominated forest following degradation. We also found that human intervention in dense PS translates into significant reductions in tree carbon stocks with initial (above and below-ground) biomass stocks (135.4 ± 4.8 Mg C ha−1) decreased by 11 and 17% following medium and high degradation. The remote sensing analysis indicates a high separability between dense PS with low degradation from all other categories. Dense PS with medium and high degradation were highly separable from most categories except for restinga forests and mixed PS. Results also showed that data from both active and passive remote sensing sensors are important for the mapping of dense PS degradation. Overall land cover classification accuracy was high (91%). Results from this pilot analysis are encouraging to further explore the use of remote sensing data and methods for monitoring dense PS degradation at broader scales in the Peruvian Amazon. Providing precise estimates on the spatial extent of dense PS degradation and on biomass and peat derived emissions is required for assessing national emissions from forest degradation in Peru and is essential for supporting initiatives aiming at reducing degradation activities

    Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

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    Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

    Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

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    LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

    A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

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    In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

    Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach

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    While small, fragmented wooded elements do not represent a large surface area in agricultural landscape, their role in the sustainability of ecological processes is recognized widely. Unfortunately, landscape ecology studies suffer from the lack of methods for automatic detection of these elements. We propose a hybrid approach using both aerial photographs and ancillary data of coarser resolution to automatically discriminate small wooded elements. First, a spectral and textural analysis is performed to identify all the planted-tree areas in the digital photograph. Secondly, an object-orientated spatial analysis using the two data sources and including a multi-resolution segmentation is applied to distinguish between large and small woods, copses, hedgerows and scattered trees. The results show the usefulness of the hybrid approach and the prospects for future ecological applications

    FOUR YEARS OF UNMANNED AERIAL SYSTEM IMAGERY REVEALS VEGETATION CHANGE IN A SUB-ARCTIC MIRE DUE TO PERMAFROST THAW

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    Warming trends in sub-arctic regions have resulted in thawing of permafrost which in turn induces change in vegetation across peatlands both in areal extent and composition. Collapse of palsas (i.e. permafrost plateaus) has also been correlated with increases in methane (CH4) emission to the atmosphere. Vegetation change provides new microenvironments that promote CH4 production and emission, specifically through plant interactions and structure. By quantifying the changes in vegetation at the landscape scale, we will be able to scale the impact of thaw on CH4 emissions in these complex climate-sensitive northern ecosystems. We combine field-based measurements of vegetation composition and Unmanned Aerial Systems (UAS) high resolution (3 cm) imagery to characterize vegetation change in a sub-arctic mire. The objective of this study is to analyze how vegetation from Stordalen Mire, Abisko, Sweden, has changed over time in response to permafrost thaw. At Stordalen Mire, we flew a fixed-wing UAS in July of each of four years, 2014 through 2017, over a 1 km x 0.5 km area. High precision GPS ground control points were used to georeference the imagery. Randomized square-meter plots were measured for vegetation composition and individually classified into one of five vegetation cover types, each representing a different stage of permafrost degradation. Using these training data, each year of imagery was classified by cover type in Google Earth Engine using a Random Forest Classifier. Textural information was extracted from the imagery, which provided additional spatial context information and improved classification accuracy. Twenty five percent of the training data were held back from the classification and used for validation, while the remaining seventy five percent of the training data were used to classify the imagery. The overall classification accuracy for 2014-2017 was 80.6%, 79.1%, 82.0%, and 82.9%, respectively. Percent cover across the landscape was calculated from each classification map and compared between years. Hummock sites, representing intact permafrost, decreased coverage by 9% from 2014-2017, while semi-wet sites increased coverage by 18%. This four-year comparison of vegetation cover indicated a rapid response to permafrost thaw. The use of a UAS allowed us to effectively capture the spatial heterogeneity of a northern peatland ecosystem. Estimation of vegetation cover types is vital in our understanding of the evolution of northern peatlands and their future role in the global carbon cycle

    Identifying and Forecasting Potential Biophysical Risk Areas within a Tropical Mangrove Ecosystem Using Multi-Sensor Data

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    Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities

    In-situ and remote monitoring of environmental water quality

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    Environmental water pollution affects human health and reduces the quality of our natural water ecosystems and resources. As a result, there is great interest in monitoring water quality and ensuring that all areas are compliant with legislation. Ubiquitous water quality monitoring places considerable demands upon existing sensing technology. The combined challenges of system longevity, autonomous operation, robustness, large-scale sensor networks, operationally difficult deployments and unpredictable and lossy environments collectively represents a technological barrier that has yet to be overcome[1]. Ubiquitous sensing envisages many aspects of our environment being routinely sensed. This will result in data streams from a large variety of heterogeneous sources, which will often vary in their volume and accuracy. The challenge is to develop a networked sensing infrastructure that can support the effective capture, filtering, aggregation and analysis of such data. This will ultimately enable us to dynamically monitor and track the quality of our environment at multiple locations. Microfluidic technology provides a route to the development of miniaturised analytical instruments that could be deployed remotely, and operate autonomously over relatively long periods of time (months–years). An example of such a system is the autonomous phosphate sensor[2] which has been developed at the CLARITY Centre, in Dublin City University. This technology, in combination with the availability of low power, reliable wireless communications platforms that can link sensors and analytical devices to online databases and servers, form the basis for extensive networks of autonomous analytical ‘stations’ or ‘nodes’ that will provide high quality information about key chemical parameters that determine the quality of our aquatic environment. The system must also have sufficient intelligence to enable adaptive sampling regimes as well as accurate and efficient decision-making responses. A particularly exciting area of development is the combination of remote satellite/aircraft based monitoring with the in-situ ground-based monitoring described above. Remote observations from satellites and aircraft can provide significant amounts of information on the state of the aquatic environment over large areas. As in-situ deployments of sensor networks become more widespread and reliable, and satellite data becomes more widely available, information from each of these sources can complement and validate the other, leading to an increased ability to rapidly detect potentially harmful events, and to assess the impact of environmental pressures on scales ranging from small river catchments to the open ocean. In this paper, we will assess the current status of these approaches, and the challenges that must be met in order to realise the vision of true internet- or global-scale monitoring of our environment. References: [1] Integration of analytical measurements and wireless communications – Current issues and future strategies. King Tong Lau, Sarah Brady, John Cleary and Dermot Diamond, Talanta 75 (2008) 606–612. [2] An autonomous microfluidic sensor for phosphate: on-site analysis of treated wastewater. John Cleary, Conor Slater, Christina McGraw and Dermot Diamond, IEEE Sensors Journal, 8 (2008) 508-515
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