English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Lespedeza cuneata  ( L. cuneata ) is an invasive legume threatening grassland ecosystems of several states in the U.S. Mapping the occurrence of invasive species is the necessary first step to control their spread. Imaging spectroscopy or hyperspectral remote sensing imagery has shown its effectiveness in detecting invasive plants. However, challenges such as limited access to hyperspectral imagery, coarse temporal resolution, high costs, and lack of standardized analysis approaches have limited their application in monitoring biological invasions. Here, we aimed to take advantage of fine spatial and temporal resolution of PlanetScope multispectral satellite data and develop an effective approach to detect  L. cuneata  in a naturally assembled grassland ecosystem. To achieve this objective, we first sampled a suite of foliar functional traits in the field and identified key traits that distinguish  L. cuneata  from native plants. We then used our airborne hyperspectral data to select vegetation indices that are associated with the key traits while focusing on those indices that are applicable to satellite-derived multispectral data. We developed seven machine learning models and a decision fusion approach using layers of vegetation indices to map  L. cuneata  during the growing season. Using our approach, we were able to map  L. cuneata  with high level of accuracy using PlanetScope multispectral imagery. Our results suggested that the optimal period for detecting  L. cuneata  in our site was mid-to-late growing season, with an overall classification accuracy of 80.49 ± 5.69% (mid-August) and 82.02 ± 4.66% (end of September), respectively. Our study contributes to developing a cost-effective solution for mapping and monitoring invasive plants, which is essential for effective control of biological invasions.

Detecting the invasive Lespedeza cuneata in grasslands using commercial small satellite imagery