Mapping sub-pixel occurrence of an alien invasive<i>Hyptis suaveolens</i>(L.) Poit. using spectral unmixing technique

Padalia, Hitendra; Kudrat, M.; Sharma, Kanika

doi:10.1080/01431161.2012.714003

Cited by 16 publications

(6 citation statements)

References 21 publications

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“…Discrimination of individual plant species is more difficult due to the complexity of species intermixing with surrounding vegetation and spectral variability within individual species. Remote sensing approaches to species-level plant mapping have been most successful when the target species possess distinctive spectra, has a large structure or grows in large stands relative to the spatial resolution of the imagery and shows vigorous population growth, and when the phenological stages of growth are taken into account during spectral signature collection (Jia et al 2011 ; Wang et al 2008 ; Andrew and Ustin 2008 ; Blumenthal et al 2009 ; Hestir et al 2008 ; Ustin et al 2002 ; Padalia et al 2013 ; Ge et al 2006 ). This presents several challenges for the remote detection of Buffel grass because most grasses are spectrally similar; the size of stands is variable and with unknown limits; vigorous growth is a response to rainfall and is not strictly seasonal; and there is a degree of intra-species variation.…”

Section: Introductionmentioning

confidence: 99%

Detecting new Buffel grass infestations in Australian arid lands: evaluation of methods using high-resolution multispectral imagery and aerial photography

Marshall

Lewis

Ostendorf

2013

Environ Monit Assess

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We assess the feasibility of using airborne imagery for Buffel grass detection in Australian arid lands and evaluate four commonly used image classification techniques (visual estimate, manual digitisation, unsupervised classification and normalised difference vegetation index (NDVI) thresholding) for their suitability to this purpose. Colour digital aerial photography captured at approximately 5 cm of ground sample distance (GSD) and four-band (visible–near-infrared) multispectral imagery (25 cm GSD) were acquired (14 February 2012) across overlapping subsets of our study site. In the field, Buffel grass projected cover estimates were collected for quadrates (10 m diameter), which were subsequently used to evaluate the four image classification techniques. Buffel grass was found to be widespread throughout our study site; it was particularly prevalent in riparian land systems and alluvial plains. On hill slopes, Buffel grass was often present in depressions, valleys and crevices of rock outcrops, but the spread appeared to be dependent on soil type and vegetation communities. Visual cover estimates performed best (r2 0.39), and pixel-based classifiers (unsupervised classification and NDVI thresholding) performed worst (r2 0.21). Manual digitising consistently underrepresented Buffel grass cover compared with field- and image-based visual cover estimates; we did not find the labours of digitising rewarding. Our recommendation for regional documentation of new infestation of Buffel grass is to acquire ultra-high-resolution aerial photography and have a trained observer score cover against visual standards and use the scored sites to interpolate density across the region.

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Section: Introductionmentioning

confidence: 99%

Detecting new Buffel grass infestations in Australian arid lands: evaluation of methods using high-resolution multispectral imagery and aerial photography

Marshall

Lewis

Ostendorf

2013

Environ Monit Assess

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“…Moreover, higher accuracies among studies specific to tropical understorey areas have been achieved either with small tree species and not shrubs [( Pouteau et al, 2011 ) – 97%; ( Lu et al, 2013 ) – 84%; ( Gavier-Pizarro et al, 2012 ) – 84%], or using abundance values from field data of large contiguous stands of the invasive species ( Padalia et al, 2012 ) and not dense understorey shrubs, or with intensive field data along with multiple image datasets and active sensors such as LIDAR or RADAR [( Asner et al, 2008b ) – 91%; ( Ghulam et al, 2014 ) – 99%] which we did not have access to. Active data sensors, although very effective, are almost impossible to acquire in India due to prohibitive costs and severe security regulations on commissioning aerial data acquisition.…”

Section: Discussionmentioning

confidence: 99%

“…While invasive tree species (Asner et al, 2008a,b) and aquatic species (Underwood et al, 2007) have been easily detected using VHR data, cryptic understorey species in forests have definitely proved harder to detect. Large, open, pure, and continuous stands of invasive shrubs have been easier to map, in temperate regions (van Lier et al, 2009; Shouse et al, 2013), as also in tropical forests (Taylor et al, 2010; Padalia et al, 2012), proving it more convenient for managers to mitigate this threat. However, a considerable proportion of invasive plant species that occur in tropical forest landscapes are shrubs which appear in the understorey (Richardson and Rejmanek, 2011), covered by the canopy of the overstorey tree species which themselves occur in several tiers.…”

Section: Introductionmentioning

confidence: 99%

Comparing Pixel and Object-Based Approaches to Map an Understorey Invasive Shrub in Tropical Mixed Forests

et al. 2017

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The establishment of invasive alien species in varied habitats across the world is now recognized as a genuine threat to the preservation of biodiversity. Specifically, plant invasions in understory tropical forests are detrimental to the persistence of healthy ecosystems. Monitoring such invasions using Very High Resolution (VHR) satellite remote sensing has been shown to be valuable in designing management interventions for conservation of native habitats. Object-based classification methods are very helpful in identifying invasive plants in various habitats, by their inherent nature of imitating the ability of the human brain in pattern recognition. However, these methods have not been tested adequately in dense tropical mixed forests where invasion occurs in the understorey. This study compares a pixel-based and object-based classification method for mapping the understorey invasive shrub Lantana camara (Lantana) in a tropical mixed forest habitat in the Western Ghats biodiversity hotspot in India. Overall, a hierarchical approach of mapping top canopy at first, and then further processing for the understorey shrub, using measures such as texture and vegetation indices proved effective in separating out Lantana from other cover types. In the first method, we implement a simple parametric supervised classification for mapping cover types, and then process within these types for Lantana delineation. In the second method, we use an object-based segmentation algorithm to map cover types, and then perform further processing for separating Lantana. The improved ability of the object-based approach to delineate structurally distinct objects with characteristic spectral and spatial characteristics of their own, as well as with reference to their surroundings, allows for much flexibility in identifying invasive understorey shrubs among the complex vegetation of the tropical forest than that provided by the parametric classifier. Conservation practices in tropical mixed forests can benefit greatly by adopting methods which use high resolution remotely sensed data and advanced techniques to monitor the patterns and effective functioning of native ecosystems by periodically mapping disturbances such as invasion.

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“…In fact, H. suaveolens, an emerging weed in Malawi, is regarded as one of the world's most noxious weeds (Padalia, Kudrat & Sharma 2013) and was ranked very highly in our surveys in terms of its negative impacts. In Australia it is considered to pose the greatest threat to rangeland biodiversity.…”

Section: Management Of Mimosa Diplotrichamentioning

confidence: 92%

Distribution of Mimosa diplotricha in eastern and southern Africa and its socioecological impacts in northern Malawi

Witt

Chimphepo²,

Beale

et al. 2020

Bothalia ABC

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Background: Mimosa diplotricha is an emerging or established weed in many parts of the world, including many countries in Africa, where it is impacting on biodiversity, crop and pasture production, and driving socio-ecological change. Objectives: To establish the current distribution of M. diplotricha in eastern and southern Africa and its impacts on livelihoods in northern Malawi. Methods: Records on current distribution were collected from roadside surveys, literature reviews and herbarium data. Household surveys were conducted in the Karonga District, Malawi, to understand its impacts on local livelihoods. Results: Mimosa diplotricha is abundant in western Ethiopia, southern Tanzania, and northern and southeastern Malawi with isolated populations in western Rwanda, Burundi, Mozambique, and on the northern shores of Lake Victoria in Uganda. Most respondents said that M. diplotricha invasions were reducing the amount of grass and shrubs in rangelands, with over half saying it reduced crop yields. This invasive plant is also reducing the availability of medicinal plants and other natural resources. Conclusions: Mimosa diplotricha has the potential to significantly expand its range in eastern Africa, and parts of southern Africa, and as such there is an urgent need to develop and implement an integrated management strategy, including biological control, to reduce the negative effects of this invasive plant on local livelihoods.

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Mapping sub-pixel occurrence of an alien invasiveHyptis suaveolens(L.) Poit. using spectral unmixing technique

Cited by 16 publications

References 21 publications

Detecting new Buffel grass infestations in Australian arid lands: evaluation of methods using high-resolution multispectral imagery and aerial photography

Detecting new Buffel grass infestations in Australian arid lands: evaluation of methods using high-resolution multispectral imagery and aerial photography

Comparing Pixel and Object-Based Approaches to Map an Understorey Invasive Shrub in Tropical Mixed Forests

Distribution of Mimosa diplotricha in eastern and southern Africa and its socioecological impacts in northern Malawi

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