Globally, biological invasions are considered as one of the major contributing factors for the loss of indigenous biological diversity. Hyperspectral remote sensing plays an important role in the detection and mapping of invasive plant species. The main objective of this study was to discriminate invasive plant species from adjacent native species using a ground-based hyperspectral sensor in two protected areas, Lehri Reserve Forest and Jindi Reserve Forest in Punjab, Pakistan. Field spectral measurements were collected using an ASD FieldSpec handheld2^TM spectroradiometer (325–1075 nm) and the discrimination between native and invasive plant species was evaluated statistically using hyperspectral indices as well as leaf wavelength spectra. Finally, spectral separability was calculated using Jeffries Matusita distance index, based on selected wavebands. The results reveal that there were statistically significant differences (p < 0.05) between the different spectral indices of most of the plant species in the forests. However, the red-edge parameters showed the highest potential (p < 0.001) to discriminate different plant species. With leaf spectral signatures, the mean reflectance between all plant species was significantly different (p < 0.05) at 562 (75%) wavelength bands. Among pairwise comparisons, invasive Leucaena leucocephala showed the best discriminating ability, with Dodonaea viscosa having 505 significant wavebands showing variations between them. Jeffries Matusita distance analysis revealed that band combinations of the red-edge region (725, 726 nm) showed the best spectral separability (85%) for all species. Our findings suggest that it is possible to identify and discriminate invasive species through field spectroscopy for their future monitoring and management. However, the upscaling of hyperspectral measurements to airborne and satellite sensors can provide a reliable estimation of invasion through mapping inside the protected areas and can help to conserve biodiversity and environmental ecosystems in the future.

Parthenium hysterophorus L. (Asteraceae), commonly known as parthenium weed, is a highly invasive weed spreading rapidly from northern to southern parts of Pakistan. The persistence of parthenium weed in the hot and dry southern districts suggests that the weed can survive under more extreme conditions than previously thought. The development of a CLIMEX distribution model, which considered this increased tolerance to drier and warmer conditions, predicted that the weed could still spread to many other parts of Pakistan as well as to other regions of south Asia. This CLIMEX model satisfied the present distribution of parthenium weed within Pakistan. When an irrigation scenario was added to the CLIMEX program, more parts of the southern districts of Pakistan (Indus River basin) became suitable for parthenium weed growth, as well as the growth of its biological control agent, Zygogramma bicolorata Pallister. This expansion from the initially predicted range was due to irrigation producing extra moisture to support its establishment. In addition to the weed moving south in Pakistan due to irrigation, it will also move north due to temperature increases. The CLIMEX model indicated that there are many more areas within South Asia that are suitable for parthenium weed growth, both under the present and a future climate scenario. Most of the south-western and north-eastern parts of Afghanistan are suitable under the current climate, but more areas are likely to become suitable under climate change scenarios. Under climate change, the suitability of southern parts of Pakistan is likely to decrease.

Whilst exotic invasive species are a major threat to natural and modified ecosystems around the world, management programs to reduce their impacts often fail due to a lack of information about their biology and how best to control them in various situations. This paper reviews the currently available information on the biology, distribution, and management options for the invasive weed Senecio madagascariensis Poir. (fireweed). In addition, we developed a model to predict the climatic suitability of this weed around the world based on the current climate. Senecio madagascariensis originates from southern Africa but it has been introduced to several other countries including Australia. Climatic suitability suggests that there are large areas around the world suitable for the weed’s growth where it is currently not present. The weed poses a major threat to livestock industries in these countries through its ability to reduce pasture production and poison animals. A range of control techniques have been used to try and manage S. madagascariensis. This paper highlights how a better understanding of the biology of S. madagascariensis can help determine the most effective treatments to impose and to further develop integrated management strategies. Besides using traditional approaches, the use of competitive pastures and more tolerant livestock (such as sheep and goats) are some of the other options recommended as part of an integrated approach. On-going research to identify host-specific biological control agents is also considered a priority.

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.

Echinochloa colona and E. crus-galli are two important annual grass weeds distributed throughout the summer cropping regions of Australia. Both species are highly problematic weeds, responsible for yield losses of up to 50% in summer grain crops. The success of Echinochloa species as weeds is attributed to their rapid growth, prolific seed production, seed dormancy and adaptability to a wide range of environments. Importantly, E. colona has evolved resistance to glyphosate in Australia, with resistant populations now widespread across the summer cropping regions. Fallow management of E. colona with glyphosate alone is risky in terms of increasing the chance of resistance and highly unsustainable; other control strategies (residual herbicides, strategic tillage, etc.) should be considered to complement herbicides. This review provides a summary of current information on the biology, ecology and management of Echinochloa species. The knowledge gaps and research opportunities identified will have pragmatic implications for the management of these species in Australian grain cropping systems.