Results 181 to 190 of about 42,098 (295)

Using Deep Learning AI to Detect Riparian Vegetation Recovery Following a Major Flood in the Brisbane River

River Research and Applications, EarlyView.
ABSTRACT Post‐flood riparian vegetation recovery demands significant attention; however, the complexity of traditional remote sensing methods often hinders environmental managers from implementing rapid vegetation monitoring. This paper developed a model using a Deep Learning Model within ArcGIS to classify and detect recovery of vegetation with high ...
Sydney O'Hare, Jinghan Li, Yongping Wei
wiley   +1 more source

Comparative analysis of traditional and Gaussian Analytical Hierarchy Process (AHP) methods for landslide susceptibility assessment. [PDF]

Sci Rep
Marques-Carvalho R, de Leon Ferreira de Carvalho ACP, Escobar-Silva EV, Quevedo RP, de Almeida CM, Santos M.   +5 more
europepmc   +1 more source

Application of a Presumptive Standards Approach to Evaluate Environmental Flows in a Highly Regulated River Basin

River Research and Applications, EarlyView.
ABSTRACT The presumptive standards approach to environmental flows offers a method to develop interim guidelines for ecological and social‐cultural flow needs. The approach is based on deriving acceptable percent‐of‐flow limits based on naturalised flows (the absence of depletion or alteration of flow), and it can be an effective precursor to a full ...
Jennifer Lento, Wendy A. Monk, Fuad Yassin, Mustakim Ali, Hayley Carlson, Saman Razavi, Timothy D. Jardine   +6 more
wiley   +1 more source

Geospatial technologies for targeting priority areas on surveillance and response of visceral leishmaniasis in São Paulo state, Brazil: embracing a One Health integrative approach. [PDF]

J Glob Health
Ferro RS, Fonseca ES, Semensati FL, Flores EF, Giufrida R, Hiramoto RM, Rangel O, de Oliveira Altieri SS, do Prado RL, Prestes-Carneiro LE.   +9 more
europepmc   +1 more source

Scale dependence in remotely sensed biodiversity: Leveraging continental‐scale imaging spectroscopy from the National Ecological Observatory Network

Remote Sensing in Ecology and Conservation, EarlyView.
Imaging spectroscopy enables large‐scale biodiversity assessment, yet spectral diversity metrics are scale dependent. Across 15 NEON ecosystems, we find that spectral richness increases sub‐linearly from 3600 m2 to 4 km2, whereas spectral divergence shows weak or inconsistent scaling with area, underscoring the importance of scale‐aware interpretation ...
Meghan T. Hayden, Matthew W. Rossi, Laura E. Dee, Kyle Kovach, Cibele H. Amaral, Jacob Nesslage, Madeline Slimp, Rachel S. Meyer, E. Natasha Stavros   +8 more
wiley   +1 more source

Functional trait syndromes structure macrophyte diversity and functional redundancy across flowing and impounded freshwater systems. [PDF]

Front Plant Sci
Vukov D, Ćuk M, Ilić M.
europepmc   +1 more source

Retrospective image analysis for long‐term demography using Google Earth imagery

Remote Sensing in Ecology and Conservation, EarlyView.
We demonstrate that high‐resolution Google Earth imagery, combined with minimal field validation, enables retrospective tracking of individual invasive plants. The image shows one of the monitored individuals of Opuntia sp. in Greece. Our approach reveals long‐term demographic patterns, recruitment dynamics, and spatial expansion without continuous ...
Erola Fenollosa, Garrett Cohen, Roberto Salguero‐Gómez   +2 more
wiley   +1 more source

Global alluvial channel patterns. [PDF]

Nat Commun
Luo Q, Park E, Latrubesse EM, Pi X, Fang H, Liu Y, Switzer AD, Li D, Qiu W, Zheng C, Liu P, Feng L.   +11 more
europepmc   +1 more source

Ground‐based robotic remote sensing for standardized biodiversity monitoring in coastal habitats

Remote Sensing in Ecology and Conservation, EarlyView.
Illustrated workflow of the proposed citizen‐to‐robot monitoring pipeline: (i) expert‐validated citizen observations are translated into AI models, (ii) deployed on a ground‐based robotic platform for proximal sensing of coastal dune habitats, (iii) enabling standardized detection of ecological targets (e.g., Pancratium maritimum & Brithys crini), and (
Giovanni Di Lorenzo, Simonetta Bagella, Micaela del Valle Rasino, Maria Laura Carranza, Manolo Garabini, Franco Angelini   +5 more
wiley   +1 more source

Research on interpretable machine learning modeling and spatial prediction of cross-landform landslide disaster mechanism: A case study of Shaanxi Province. [PDF]

PLoS One
Han M, Yang Y.
europepmc   +1 more source