Monitoring and Mapping Invasive Aquatic Weed Salvinia molesta using Multispectral Remote Sensing Technique in Tumaria Wetland of Uttarakhand, India

A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

“…Together, these technological and algorithm advancements may prove beneficial for the monitoring of typically highly variable aquatic vegetation cover [16], and for the detection of newly spreading IAAP invasions, at a lower cost than equivalent field work [17,18]. Moreover, these advancements may allow for the monitoring of satellite-detectable waterbodies and their associated infestations across national levels, surpassing the capacity of previous methods for large scale water hyacinth (Pontederia crassipes, previously Eichhornia crassipes) discrimination [19][20][21][22][23]. Subsequently, if IAAP infestations and their associated management efforts are closely monitored across large areas, effective management that allows for the efficient allocation of limited resources may be promoted [24][25][26].…”

“…This bias toward terrestrial systems persists despite freshwater ecosystems being under greater invasion risk per unit area [33,34]. Moreover, this pattern is evident when considering the limited number of studies showcasing the use of freely available medium spatial resolution (<30 m) multispectral satellite data, to locate IAAP species (for example, [19,23,35,36]). Ultimately there are still a limited number of studies that forecast risk of IAAP species spread (for example, [37]), or that aim to apply satellite data approaches end-to-end to investigate the drivers and impacts of IAAPs (for example, [38]).…”

“…Thus, despite the benefits to be gained from EO-based IAAP monitoring at large extents [21,39], such an approach has yet to be realized. This may largely be attributed to previous studies that rely on a single supervised model to simultaneously discriminate water hyacinth from multiple surrounding land cover types [19][20][21][22][23]. Consequently, these studies highlight the erroneous detection of surrounding land cover as water hyacinth [19][20][21]23,35].…”

“…This may largely be attributed to previous studies that rely on a single supervised model to simultaneously discriminate water hyacinth from multiple surrounding land cover types [19][20][21][22][23]. Consequently, these studies highlight the erroneous detection of surrounding land cover as water hyacinth [19][20][21]23,35]. This source of error, together with the limited, local distribution of field locality data hinders the detection of any IAAP within waterbodies distributed across large areas in a reliable manner.…”