2021
DOI: 10.1007/s10530-021-02543-2
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Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees

Abstract: Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive nonnative trees

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Cited by 18 publications
(9 citation statements)
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“…For instance, overall accuracy greater than 85% was obtained using a classification model based on a single multispectral Landsat 8 image [23,24]. Similar results were obtained using multitemporal analysis or a combination of optical data with lidar or radar data, with overall accuracies between 70 and 85% [25][26][27]. In a previous study, we used the pixel-based classification of a very high-spatial-resolution Pleiades image to detect Acacia mearnsii forest stands on Reunion Island [28].…”
Section: Introductionsupporting
confidence: 60%
“…For instance, overall accuracy greater than 85% was obtained using a classification model based on a single multispectral Landsat 8 image [23,24]. Similar results were obtained using multitemporal analysis or a combination of optical data with lidar or radar data, with overall accuracies between 70 and 85% [25][26][27]. In a previous study, we used the pixel-based classification of a very high-spatial-resolution Pleiades image to detect Acacia mearnsii forest stands on Reunion Island [28].…”
Section: Introductionsupporting
confidence: 60%
“…Employing visible-shortwave infrared reflectance values as independent variables, a RF At the first level, to enhance the distribution and number of the samples for large-scale modeling, AVIRIS-NG data were used. This hyperspectral data includes much higher spectral information compared to Sentinel-2 multispectral data, in addition to much higher spatial resolution of 5 m. There have been several studies using AVIRIS-NG besides Sentinel spaceborne data to enhance the produced results [61][62][63]. RF is a robust classifier and regression method for wetland classification and regression [64].…”
Section: Agb Regression Modelsmentioning
confidence: 99%
“…Different algorithms produce maps with different accuracies for heterogeneous spatial systems [24]; the following algorithms have been used most often to identify invasive plants: spectral angle mapper (SAM; [33,34]; OA: 63-95%), mixture tuned match filtering (MTMF; [35,36]; OA: 64-90%), random forest (RF; [37,38]; OA: 84-97%), support vector machine (SVM; [22,38]; OA: 92-98%), and neural networks (NNs; [22,39]; OA: 97-99%). Owing to the high accuracy of invasive vegetation classification, the most popular methods are machine learning (ML) algorithms, such as SVM [40], RF [41], and NNs [24].…”
Section: Introductionmentioning
confidence: 99%