Mapping Lantana camara and Leucaena leucocephala in Protected Areas of Pakistan: A Geo-Spatial Approach

Abstract

Invasive alien plants are considered as one of the major causes of loss of native biodiversity around the world. Remote sensing provides an opportunity to identify and map native and invasive species using accurate spectral information. The current study was aimed to evaluate PlanetScope (3 m) and Sentinel (10 m) datasets for mapping the distribution of native and invasive species in two protected areas in Pakistan, using machine learning (ML) algorithms. The multispectral data were analysed with the following four ML algorithms (classifiers)—random forest (RF), Gaussian mixture model (GMM), k-nearest neighbour (KNN), and support vector machine (SVM)—to classify two invasive species, Lantana camara L. (common lantana) and Leucaena leucocephala L. The (Ipil-ipil) Dzetsaka plugin of QGIS was used to map these species using all ML algorithms. RF, GMM, and SVM algorithms were more accurate at detecting both invasive species when using PlanetScope imagery rather than Sentinel. Random forest produced the highest accuracy of 64% using PlanetScope data. Lantana camara was the most dominating plant species with 23% cover, represented in all thematic maps. Leucaena leucocpehala was represented by 7% cover and was mainly distributed in the southern end of the Jindi Reserve Forest (Jhelum). It was not possible to discriminate native species Dodonea viscosa Jacq. (Snatha) using the SVM classifier for Sentinel data. Overall, the accuracy of PlanetScope was slightly better than Sentinel in term of species discrimination. These spectral findings provide a reliable estimation of the current distribution status of invasive species and would be helpful for land managers to prioritize invaded areas for their effective management.

UAV-Based Classification of Intercropped Forage Cactus: A Comparison of RGB and Multispectral Sample Spaces Using Machine Learning in an Irrigated Area

“…The Gaussian Mixture Model algorithm showed the lowest performance in contrast to K-Nearest Neighbors and Random Forest, possibly because it was not developed as a classification algorithm, contrasting with the results obtained by Iqbal et al [22]. Classification algorithms are subject to continuous updates and improvements, reflecting a constant evolution in accuracy.…”

“…The differences presented in comparison to the studies in the literature are significant, considering the spatial resolution of approximately 2 cm of our study and the application in a semiarid region, which presents significant differences in the performance of K-Nearest Neighbors for region classification. The Iqbal et al [22] study focused on the application of the Dzetsaka tool with the use of algorithms for classification with satellite images. The results were better for the PlanetScope satellite constellation compared to Sentinel-2, given that the spatial resolution of PlanetScope is 3 m, while the Sentinel-2 is 10 m. The difference in resolution is even more evident in our study, which produced a pixel size of 2.1 cm, significantly affecting the algorithm ranking and performance.…”

“…Studies such as Yadav et al [21] highlight the potential of remote sensing in monitoring crop health, weed control, and estimating evapotranspiration. Iqbal et al [22] developed machine learning (ML) applications for crop classification to identify invasive plants in Pakistan, demonstrating the high performance of Gaussian Mixture Model (GMM), support vector machine (SVM), and Random Forest (RF) algorithms. Meanwhile, Sivakumar and TYJ [23] detected weeds from high-resolution images, emphasizing the potential of Unmanned Aerial Vehicles (UAVs) in agricultural research.…”

“…Utilizing spectral responses and crop classification allows adjusting management strategies for enhancing productivity, particularly in the case of intercropped cultivations. Despite recent research employing multispectral UAV remote sensing, exemplified by the study conducted by Lourenço et al [12] for highly heterogeneous fields, and the integration of machine learning for agricultural mapping through remote sensing, as demonstrated in the work of Iqbal et al [22], investigations that jointly incorporate artificial intelligence and UAV images for crop classification are still in their early stages in the literature. Specifically, there is a significant need for Machine Learning (ML) applications to interpret multispectral and RGB images acquired by UAVs for the automatic classification of intercropped forage cacti in irrigated areas of the Brazilian semiarid.…”

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy

“…Chemometric analysis using UV-VIS-NIR-SWIR hyperspectral data is an emerging approach to unravel the underlying relationship between spectral reflectance patterns and biochemical constituents of plant leaves [8,9]. By integrating spectral data with advanced statistical methods, such as principal component analysis (PCA) and selecting wavelengths and bands most responsively, it becomes feasible to detect subtle differences in leaf biochemistry, even before they manifest as visible symptoms [10].…”