Relative importance of hydrological variables in predicting the habitat suitability of<i>Euryale ferox</i>Salisb.

Kim, Ji Yoon; Kim, Gu-Yeon; Do, Yuno; Park, Hee Sun; Joo, Gea‐Jae

doi:10.1093/jpe/rtw106

Cited by 1 publication

(1 citation statement)

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“…The literature review supports that, as the size of the site increases, the scale of analysis decreases. For examples, (i) peatlands were classified at a metric resolution on a site of a few ha using QuickBird and WorldView-3 satellite images [47]; (ii) Kim et al [58] and Doninck et al [59] classified plant species using 30 m Landsat images across South Korea and the Amazon, respectively; and (iii) biomes were mapped over the entire earth using kilometric-resolution MODIS products [60].…”

Section: Site Extent and Spatial Scalementioning

confidence: 99%

One-Class Classification of Natural Vegetation Using Remote Sensing: A Review

Rapinel

Hubert‐Moy

2021

Remote Sensing

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Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.

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