Publication Date:
2024-04-25
Description:
〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Vegetation classification is an essential prerequisite for understanding vegetation‐water relations at a range of spatial scales. However, in site‐specific applications, such classifications were mostly based on a single Unmanned Aerial Vehicle (UAV) flight, which can be challenging in grasslands and/or herbaceous‐dominated systems, as those communities are small in size and highly mixed. Here, we conducted monthly UAV flights for two years in a riparian wetland in Germany, with acquired imagery used for vegetation classification on a monthly basis under different strategies (with or without auxiliary information from other flights) to increase understanding in ecohydrology. The results show that multi‐flight‐based classification outperformed single‐flight‐based classification due to the higher classification accuracy. Moreover, improved sensitivity of temporal changes in community distribution highlights the benefits of multi‐flight‐based classification ‐ providing a more comprehensive picture of community evolution. From reference to the monthly community distribution, we argue that a combination of two or three flights in early‐ and late‐summer is enough to achieve comparable results to monthly flights, while mid‐summer would be a better timing in case only one flight is scheduled. With such detailed vegetation mapping, we further interpreted the complex spatio‐temporal heterogeneity in NDVI and explored the dominant areas and developmental progress of each community. Impacts from management (mowing events) were also evaluated based on the different responses between communities in two years. Finally, we explored how such vegetation mapping could help understand landscape ecohydrology, and found that the spatio‐temporal distribution of minimal soil moisture was related to NDVI peaks of local community, while grass distribution was explained by both topography and low moisture conditions. Such bi‐directional relationships proved that apart from contributing to an evidence base for wetland management, multi‐flight UAV vegetation mapping could also provide fundamental insights into the ecohydrology of wetlands.〈/p〉
Description:
Chinese Scholarship Council (CSC)
Description:
Einstein Foundation Berlin and Berlin University Alliance
Description:
Leverhulme Trust
http://dx.doi.org/10.13039/501100000275
Keywords:
ddc:551.48
;
ecohydrology
;
remote sensed vegetation dynamics
;
soil moisture
;
UAV
;
unmanned aerial vehicles
;
wetlands
Language:
English
Type:
doc-type:article
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