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The impetus for the development of this Field Guide came about as a result of pleas from the community around the village of Doldol, Laikipia County, to initiate a control programme for Australian prickly pear [Opuntia stricta (Haw.) Haw.; Fabaceae], an invasive plant which was having a dramatic impact on livelihoods. However, a number of other exotic plants, which were less widespread, but had the potential of becoming invasive, were not seen as a potential problem. In order to avoid a similar situation from arising in the future, the community expressed a need for a Field Guide, which would include descriptions of naturalized and invasive species already present in, and those that were most likely to invade Laikipia County and, information on how best to manage them. An additional impetus was to contribute to the four main objectives of the National Strategy and Action Plan for the Management of Invasive Species in Kenya's Protected Areas. The Field Guide contributes in some or other way to all of these objectives which are to (i) Enhance awareness of invasive species to relevant actors; (ii) Prevent new invasions, manage established invasions and rehabilitate degraded habitats; (iii) Enhance research, monitoring and information management on invasive species; and (iv) Enhance capacity, resource mobilization and coordination. Extensive surveys revealed the presence of a number of introduced plant species which had escaped cultivation and established populations in the 'wild' to the detriment of natural resources and the people that depend on them. Introduced succulents, especially those in the genus Opuntia (Cactaceae), were found to be the most widespread and abundant invasive species in the semi-arid regions in the north and east of Laikipia County. Other succulents, those in the genus Bryophyllum (Crassulaceae), were also found to have escaped cultivation and were locally abundant. In the higher rainfall areas to the west and southwest, introduced trees such as black wattle (Acacia mearnsii De Wild.; Fabaceae) and Australian blackwood (Acacia melanoxylon R. Br.; Fabaceae) and the shrubs/climbers, Mauritius thorn [Caesalpinia decapetala (Roth) Alston; Fabaceae] and yellow cestrum (Cestrum aurantiacum Lindl.; Solanaceae), were invasive. Introduced plants, which have the potential to become problematic in Laikipia, unless eradicated or controlled, have also been included in the Guide. This includes species such as famine weed (Parthenium hysterophorus L.; Asteraceae) and 'mathenge' [Prosopis juliflora (Sw.) DC.; Fabaceae], which are already abundant in areas adjoining the County.

Invasive plants. --- Plant introduction. --- Weeds.

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In recent decades, there has been an increase in the development of strategies for water ecosystem mapping and monitoring. Overall, this is primarily due to legislative efforts to improve the quality of water bodies and oceans. Remote sensing has played a key role in the development of such approaches—from the use of drones for vegetation mapping to autonomous vessels for water quality monitoring. Within the specific context of vegetation characterization, the wide range of available observations—from satellite imagery to high-resolution drone aerial imagery—has enabled the development of monitoring and mapping strategies at multiple scales (e.g., micro- and mesoscales). This Special Issue, entitled “Novel Advances in Aquatic Vegetation Monitoring in Ocean, Lakes and Rivers”, collates recent advances in remote sensing-based methods applied to ocean, river, and lake vegetation characterization, including seaweed, kelp, submerged and emergent vegetation, and floating-leaf and free-floating plants. A total of six manuscripts have been compiled in this Special Issue, ranging from area mapping substrates in riverine environments to the identification of macroalgae in marine environments. The work presented leverages current state-of-the-art methods for aquatic vegetation monitoring and will spark further research within this field.

bottom reflectance --- aquatic vegetation --- normalized difference vegetation index (NDVI) --- Lake Ulansuhai --- concave–convex decision function --- radiative transfer --- methodological comparison --- remote sensing extraction --- invasive plants --- CAS S. alterniflora --- spectroscopy --- China --- nuclear power station --- floating algae index (FAI) --- Landsat OLI --- Spartina alterniflora --- substrate --- unmanned aerial vehicle --- Lake Baikal --- reflectance --- 1st derivative --- seaweed --- remote sensing --- WorldView-2 --- species discrimination --- WorldView-3 --- water-column correction --- Selenga River Delta --- macroalgae --- object-based image analysis --- seaweed enhancing index (SEI) --- freshwater wetland --- GF-1 satellite --- river

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Despite the significant impacts of alien plant species (IAS), there has not been a concerted effort to tackle the problem across the region. This can mainly be ascribed to a lack of policy, little awareness and limited capacity at a national and regional level. The UN Environment-Global Environment Facility project, 'Removing Barriers to Invasive Species Management in Production and Protection Forests in SE Asia', which was active in Cambodia, Indonesia, the Philippines and Vietnam, identified these barriers and produced this Guide which will go a long way to creating awareness about invasive plants, their impacts and how best to manage them. This Guide will serve as an invaluable aid in the identification, mapping, monitoring, and management of IAS that are already present in ASEAN member states, or which may become problematic in the future, due to increased trade and travel, economic development and climate change. It is hoped that this Guide would trigger similar efforts in other countries in Southeast Asia as the region moves toward socio-economic integration.

Plants --- Invasive plants. --- Plant invaders --- Alien plants --- Plant invasions --- Flora --- Plant kingdom --- Plantae --- Vascular plants --- Vegetable kingdom --- Vegetation --- Wildlife --- Organisms --- Botany --- invasives --- nonindigenous species --- Viet Nam --- Cambodia --- Indochina --- Least Developed Countries --- weed control --- ASEAN Countries --- recommendations --- South East Asia --- Southeast Asia --- Vietnam --- awareness --- exotic species --- guidelines --- weeds --- Kampuchea --- invasive organisms --- eukaryotes --- introduced organisms --- mapping --- nonindigenous organisms --- Philippines --- cartography --- monitoring --- APEC countries --- invasive species --- non-native species --- invasive alien species --- Asia --- exotic organisms --- Khmer Republic --- Developing Countries --- alien invasive species --- introduced species --- non-indigenous organisms --- non-indigenous species --- non-native organisms --- Indonesia

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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.

Research & information: general --- Geography --- geographic information system (GIS) --- pocket beaches --- coastal management --- Interreg --- climate change --- remote sensing --- drone --- Sicily --- Malta --- Gozo --- Comino --- systematic literature review --- anomaly intrusion detection --- deep learning --- IoT --- resource constraint --- IDS --- evapotranspiration --- penman-monteith equation --- artificial neural network --- canopy conductance --- Ziz basin --- water quality --- satellite image analysis --- modeling approach --- nitrate --- dissolved oxygen --- chlorophyll a --- time series analysis --- environmental monitoring --- water extraction --- modified normalized difference water index (MNDWI) --- machine learning algorithm --- hyperspectral --- proximal sensing --- panicle initiation --- normalized difference vegetation index (NDVI) --- green ring --- internode-elongation --- Sentinel 1 and 2 --- Copernicus Sentinels --- crop classification --- food security --- agricultural monitoring --- data analysis --- SAR --- random forest --- 3D bale wrapping method --- equal bale dimensions --- mathematical model --- minimal film consumption --- optimal bale dimensions --- round bales --- Sentinel-2 --- SVM --- RF --- Boufakrane River watershed --- irrigation requirements --- water resources --- sustainable land use --- agriculture --- invasive plants --- precision agriculture --- rice farming --- site-specific weed management --- nitrogen prediction --- 1D convolution neural networks --- cucumber --- crop yield improvement --- mango leaf --- CCA --- vein pattern --- leaf disease --- cubic SVM --- chlorophyll-a concentration --- transfer learning --- overfitting --- data augmentation --- guava disease --- plant disease detection --- crops diseases --- entropy --- features fusion --- machine learning --- object-based classification --- density estimation --- histogram --- land use --- crop fields --- soil tillage --- data fusion --- multispectral --- sensor --- probe --- temperature profile --- forest roads --- simulation --- autonomous robots --- smart agriculture --- environmental protection --- photogrammetry --- path planning --- internet of things --- modeling --- convolutional neural networks --- machine vision --- computer vision --- modular robot --- selective spraying --- vision-based crop and weed detection --- Faster R-CNN --- YOLOv5 --- band selection --- CNN --- NDVI --- hyperspectral imaging --- crops --- urban flood --- Sentinel-1a --- Synthetic Aperture Radar (SAR) --- 3D Convolutional Neural Network --- multi-temporal data --- land use classification --- GIS --- Coatzacoalcos --- algorithms --- clustering --- pest control --- site-specific --- virtual pests --- rice plant --- weed --- hyperspectral imagery --- sustainable agriculture --- green technologies --- Internet of Things --- natural resources --- sustainable environment --- IoT ecosystem --- hyperspectral remoting sensing --- crop mapping --- image classification --- deep transfer learning --- hyperparameter optimization --- metaheuristic --- soil attribute --- ordinary Kriging --- rational sampling numbers --- spatial heterogeneity --- sampling --- soil pH --- spatial variation --- ordinary kriging --- Land Use/Land Cover --- LISS-III --- Landsat --- Vision Transformer --- Bidirectional long-short term memory --- Google Earth Engine --- Explainable Artificial Intelligence