583 research outputs found

    Remote detection of invasive alien species

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    The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail

    Multitemporal Remote Sensing Based on an FVC Reference Period Using Sentinel-2 for Monitoring Eichhornia crassipes on a Mediterranean River

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    International audienceInvasive aquatic plants are a serious global ecological and socio-economic problem because they can cause local extinction of native species and alter navigation and fishing. Eichhornia crassipes (water hyacinth) is a dangerous invasive floating plant that is widely distributed throughout the world. In Lebanon, it has spread since 2006 in the Al Kabir River. Remote sensing techniques have been widely developed to detect and monitor dynamics and extents of invasive plants such as water hyacinth over large areas. However, they become challenging to use in narrow areas such as the Al Kabir River and we developed a new image-analysis method to extract water hyacinth areas on the river. The method is based on a time series of a biophysical variable obtained from Sentinel-2 images. After defining a reference period between two growing cycles, we used the fractional vegetation cover (FVC) to estimate the water hyacinth surface area in the river. This method makes it possible to monitor water hyacinth development and estimate the total area it colonizes in the river corridor. This method can help ecologists and other stakeholders to map invasive plants in rivers and improve their control

    Earth observation for water resource management in Africa

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    Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

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    Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

    Mapping and monitoring the Akagera wetland in Rwanda

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    Wetland maps are a prerequisite for wetland development planning, protection, and restoration. The present study aimed at mapping and monitoring Rwanda's Akagera Complex Wetland by means of remote sensing and geographic information systems (GIS). Landsat data, spanning from 1987 to 2015, were acquired from different sensor instruments, considering a 5-year interval during the dry season and the shuttle radar topographic mission (SRTM) digital elevation model (30-m resolution) was used to delineate the wetland. The mapping and delineation results showed that the wetland narrowly extends along the Rwanda-Tanzania border from north to south, following the course of Akagera River and the total area can be estimated at 100,229.76 ha. After waterbodies that occupy 30% of the wetland's surface area, hippo grass and Cyperus papyrus are also predominant, representing 29.8% and 29%, respectively. Floodplain and swamp forest have also been inventoried in smaller proportions. While the wetland extent has apparently remained stable, the inhabiting waterbodies have been subject to enormous instability due to invasive species. Lakes, such as Mihindi, Ihema, Hago and Kivumba have been shrinking in extent, while Lake Rwanyakizinga has experienced a certain degree of expansion. This study represents a consistent decision support tool for Akagera wetland management in Rwanda

    Understanding seasonal dynamics of invasive water hyacinth (eichhornia crassipes) in the greater letaba river system using sentinel-2 satellite data

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    Water hyacinth (Eichhornia crassipes) is one of the most aggressive and lethal free-floating aquatic weed that degrades and chokes freshwater ecosystems and threatens aquatic life. Early detection and up-to-date information regarding its distribution is, therefore, crucial in understanding its spatial configuration and propagation rate. The present study, thus, sought to map the seasonal dynamics of invasive water hyacinth, in Greater Letaba river system in Limpopo Province, South Africa, using Sentinel-2 data and Linear Discriminant Analysis (LDA). Classification test results showed that seasonal water hyacinth distribution patterns can be accurately detected and mapped, using Sentinel-2 data with high accuracies. Water hyacinth was mapped with an overall accuracy of 80.79% during the wet season, and 79.04% during the dry season, with kappa coefficients of 0.76 and 0.724, respectively, using combined vegetation indices and spectral bands

    Monitoring the spread of water hyacinth (Pontederia crassipes): challenges and future developments

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    Water hyacinth (Pontederia crassipes, also referred to as Eicchornia crassipes) is one of the most invasive weed species in the world, causing significant adverse economic and ecological impacts, particularly in tropical and sub-tropical regions. Large scale real-time monitoring of areas of chronic infestation is critical to formulate effective control strategies for this fast spreading weed species. Assessment of revenue generation potential of the harvested water hyacinth biomass also requires enhanced understanding to estimate the biomass yield potential for a given water body. Modern remote sensing technologies can greatly enhance our capacity to understand, monitor and estimate water hyacinth infestation within inland as well as coastal freshwater bodies. Readily available satellite imagery with high spectral, temporal and spatial resolution, along with conventional and modern machine learning techniques for automated image analysis, can enable discrimination of water hyacinth infestation from other floating or submerged vegetation. Remote sensing can potentially be complemented with an array of other technology-based methods, including aerial surveys, ground-level sensors, and citizen science, to provide comprehensive, timely and accurate monitoring. This review discusses the latest developments in the use of remote sensing and other technologies to monitor water hyacinth infestation, and proposes a novel, multi-modal approach that combines the strengths of the different methods

    Assessing the use of imagery from sentinel-2 and an unmanned aerial vehicle to monitor Nymphoides peltata in an Oklahoma reservoir

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    Remote sensing of aquatic invasive plants has been relatively understudied for treatment monitoring applications. Invasive aquatic plants cause ecological distress as well as millions of dollars in damages and lost utility value and ecosystem services. Yellow floating heart (Nymphoides peltata) is a floating leaved macrophyte native to Southeast Asia and the Mediterranean. This plant has prolific spread potential and can create dense canopies that shade out other organisms. It was reported in Lake Carl Blackwell of Oklahoma in 2014. It covered over 20 hectares at its peak in 2019. The herbicide ProcellaCOR was applied to the infestation in the summer of 2019. The purpose of this research was to use Sentinel-2 satellite data and an unmanned aerial vehicle equipped with a MicaSense RedEdge-M camera to monitor the infestation and compare the sensors from spatial and spectral parameters. This can help lake managers integrate remote sensing tools into their monitoring programs in a cost-effective manner. A Sentinel-2 dataset was downloaded from the USGS EarthExplorer. UAV data was collected and processed in AgiSoft Structure-from-Motion. The Sentinel-2 and UAV datasets were classified by Maximum Likelihood Classification to compare ability to detect and delineate N. peltata with overall accuracies of 96.1% (kappa = 0.88) and 94.3% (0.80), respectively. The spatial extent was manually digitized on all available data and compared with regression analysis with a significantly high relationship (R2 = 0.94; p < 0.001). This measure also indicated a 91% reduction of the infestation after the herbicide application, a reduction of almost 2% lake coverage to less than 0.1%, due to herbicide treatment. The sensors were also compared in their measurements of the Normalized Difference Vegetation Index (R2 = 0.40; p = 0.13) and the Fractional Vegetative Index (R2 = 0.39; p = 0.13) both of which had low significance. The spatial data from Sentinel-2 was used to make an estimation of the potential economic impact caused by this infestation by correlating it to the average lake value

    Application of LANDSAT to the surveillance of lake eutrophication in the Great Lakes basin

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    The author has identified the following significant results. A step-by-step procedure for establishing and monitoring the trophic status of inland lakes with the use of LANDSAT data, surface sampling, laboratory analysis, and aerial observations were demonstrated. The biomass was related to chlorophyll-a concentrations, water clarity, and trophic state. A procedure was developed for using surface sampling, LANDSAT data, and linear regression equations to produce a color-coded image of large lakes showing the distribution and concentrations of water quality parameters, causing eutrophication as well as parameters which indicate its effects. Cover categories readily derived from LANDSAT were those for which loading rates were available and were known to have major effects on the quality and quantity of runoff and lake eutrophication. Urban, barren land, cropland, grassland, forest, wetlands, and water were included
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