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Full-Text Articles in Life Sciences
A Review Of Landcover Classification With Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability And Transferability, Rongjun Qin, Tao Liu
A Review Of Landcover Classification With Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability And Transferability, Rongjun Qin, Tao Liu
Michigan Tech Publications
As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a …
Fine-Scale Mapping Of Natural Ecological Communities Using Machine Learning Approaches, Parth Bhatt, Ann Maclean, Yvette Dickinson, Chandan Kumar
Fine-Scale Mapping Of Natural Ecological Communities Using Machine Learning Approaches, Parth Bhatt, Ann Maclean, Yvette Dickinson, Chandan Kumar
Michigan Tech Publications
Remote sensing technology has been used widely in mapping forest and wetland communities, primarily with moderate spatial resolution imagery and traditional classification techniques. The success of these mapping efforts varies widely. The natural communities of the Laurentian Mixed Forest are an important component of Upper Great Lakes ecosystems. Mapping and monitoring these communities using high spatial resolution imagery benefits resource management, conservation and restoration efforts. This study developed a robust classification approach to delineate natural habitat communities utilizing multispectral high-resolution (60 cm) National Agriculture Imagery Program (NAIP) imagery data. For accurate training set delineation, NAIP imagery, soils data and spectral …
Characterizing Boreal Peatland Plant Composition And Species Diversity With Hyperspectral Remote Sensing, Mara Y. Mcpartland, Michael J. Falkowski, Jason R. Reinhardy, Evan Kane, Randall K Kolka, Merritt R. Turetsky, Et Al.
Characterizing Boreal Peatland Plant Composition And Species Diversity With Hyperspectral Remote Sensing, Mara Y. Mcpartland, Michael J. Falkowski, Jason R. Reinhardy, Evan Kane, Randall K Kolka, Merritt R. Turetsky, Et Al.
Michigan Tech Publications
Peatlands, which account for approximately 15% of land surface across the arctic and boreal regions of the globe, are experiencing a range of ecological impacts as a result of climate change. Factors that include altered hydrology resulting from drought and permafrost thaw, rising temperatures, and elevated levels of atmospheric carbon dioxide have been shown to cause plant community compositional changes. Shifts in plant composition affect the productivity, species diversity, and carbon cycling of peatlands. We used hyperspectral remote sensing to characterize the response of boreal peatland plant composition and species diversity to warming, hydrologic change, and elevated CO2. …