Mapping of understorey invasive plant species clusters of Lantana camara and Chromolaena odorata using airborne hyperspectral remote sensing

Kishore, Bodi Surya Pratap Chandra; Kumar, Amit; Saikia, Purabi; Lele, Nikhil; Srivastava, Parul; Pulla, Sandeep; Suresh, H. S.; Bhattarcharya, Bimal Kumar; Khan, Mohammed Latif; Sukumar, Raman

doi:10.1016/j.asr.2022.12.026

Cited by 4 publications

(1 citation statement)

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“…For instance, studies have used both multispectral and hyperspectral imagery collected by airborne and satellite sources to detect woody and grass invasive species [6,42,43]. The commonly used data analysis methods in these remote sensing applications are feature-driven and rely on feature metrics derived from imagery [9,22,44,45]. Although these feature-driven approaches obtained satisfactory performance (over 85% overall accuracy) in invasive species detection studies, defining a series of appropriate feature metrics is challenging.…”

Section: Introductionmentioning

confidence: 99%

Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network

Xing,

An,

Guo

et al. 2024

Remote Sensing

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Invasive noxious weed species (INWS) are typical poisonous plants and forbs that are considered an increasing threat to the native alpine grassland ecosystems in the Qinghai–Tibetan Plateau (QTP). Accurate knowledge of the continuous cover of INWS across complex alpine grassland ecosystems over a large scale is required for their control and management. However, the cooccurrence of INWS and native grass species results in highly heterogeneous grass communities and generates mixed pixels detected by remote sensors, which causes uncertainty in classification. The continuous coverage of INWS at the pixel level has not yet been achieved. In this study, objective 1 was to test the capability of Senginel-2 imagery at estimating continuous INWS cover across complex alpine grasslands over a large scale and objective 2 was to assess the performance of the state-of-the-art convolutional neural network-based regression (CNNR) model in estimating continuous INWS cover. Therefore, a novel CNNR model and a random forest regression (RFR) model were evaluated for estimating INWS continuous cover using Sentinel-2 imagery. INWS continuous cover was estimated directly from Sentinel-2 imagery with an R2 ranging from 0.88 to 0.93 using the CNNR model. The RFR model combined with multiple features had a comparable accuracy, which was slightly lower than that of the CNNR model, with an R2 of approximately 0.85. Twelve green band-, red-edge band-, and near-infrared band-related features had important contributions to the RFR model. Our results demonstrate that the CNNR model performs well when estimating INWS continuous cover directly from Sentinel-2 imagery, and the RFR model combined with multiple features derived from the Sentinel-2 imager can also be used for INWS continuous cover mapping. Sentinel-2 imagery is suitable for mapping continuous INWS cover across complex alpine grasslands over a large scale. Our research provides information for the advanced mapping of the continuous cover of invasive species across complex grassland ecosystems or, more widely, terrestrial ecosystems over large spatial areas using remote sensors such as Sentinel-2.

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