Examining the Spectral Separability of Prosopis glandulosa from Co-Existent Species Using Field Spectral Measurement and Guided Regularized Random Forest

Abstract

Abstract:The invasive taxa of Prosopis is rated the world's top 100 unwanted species, and a lack of spatial data about the invasion dynamics has made the current control and monitoring methods unsuccessful. This study thus tests the use of in situ spectroscopy data with a newly-developed algorithm, guided regularized random forest (GRRF), to spectrally discriminate Prosopis from coexistent acacia species (Acacia karroo, Acacia mellifera and Ziziphus mucronata) in the arid environment of South Africa. Results show that GRRF was able to reduce the high dimensionality of the spectroscopy data and select key wavelengths (n = 11) for discriminating amongst the species. These wavelengths are located at 356.3 nm, 468.5 nm, 531.1 nm, 665.2 nm, 1262.3 nm, 1354.1 nm, 1361.7 nm, 1376.9 nm, 1407.1 nm, 1410.9 nm and 1414.6 nm. The use of these selected wavelengths increases the overall classification accuracy from 79.19% and a Kappa value of 0.7201 when using all wavelengths to 88.59% and a Kappa of 0.8524 when the selected wavelengths were used. Based on our relatively high accuracies and ease of use, it is worth considering the GRRF method for reducing the high dimensionality of spectroscopy data. However, this assertion should receive considerable additional testing and comparison before it is accepted as a substitute for reliable high dimensionality reduction.

Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem

“…Prosopis juliflora (Swartz DC. ), hereafter referred to as Prosopis, has been introduced to different parts of the world with the aim of providing benefits to rural people, such as the production of fuelwood, charcoal, or construction material (Engda, 2009;Haji & Mohammed, 2013;Mureriwa, Adam, Sahu, & Tesfamichael, 2016;Pasiecznik & Henry Doubleday Research Association, 2001). Like numerous other introduced plants, Prosopis has become invasive in many places and is increasingly known for its negative ecological and socio-economic impacts (Shackleton, Le Maitre, van Wilgen et al, 2015a;Shackleton, Le Maitre, van Wilgen et al, 2015b;van Wilgen & Wannenburgh, 2016).…”

A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

“…Here, we describe the modified version of DoTRules for hyperspectral image classification, before demonstrating its application in three different study areas. We quantify the accuracy of DoTRules for hyperspectral image classification, and compare the results against some popular state-of-the-art ensemble approaches, i.e., extreme gradient boosting (XGBoost) [40,41], random forest (RF) [1,[42][43][44][45], rotation forests (RoFs) [46][47][48][49], regularised random forest (RRF) [50,51], as well as two non-ensemble algorithms, namely, support vector machine (SVM) [52][53][54][55][56], and deep belief network (DBN) [57,58] as the classic deep learning method. Although SVM and DBN are not ensemble methods, they are included in our comparison because of their popularity, as they have been repeatedly used in recent hyperspectral image classification studies using Indian Pines, Salinas and Pavia University datasets.…”

Spectral Discrimination of Archaeological Sites Previously Occupied by Farming Communities Using In Situ Hyperspectral Data

“…e GRRF algorithm guides the feature selection process in the regularised RF using importance scores from the normal RF [66]. To date, only two studies have used GRRF for the reduction of high dimensionality in hyperspectral data for vegetation studies [67,68].…”