Statistics and Data Analysis in Geology

Summary 1. Government departments, environmental managers and conservationists are all facing escalating pressure to address and resolve a diversity of invasive alien species (IAS) problems. Yet much research to date is primarily concerned with quantifying the scale of the problem rather than delivering robust solutions and has not adequately addressed all stages of the invasion process, and only a few studies embrace the ecosystem approach. 2. Three successive steps, prevention, eradication and control, form the cornerstones of recommended best practices aimed at managing IAS. The goal of such actions is the restoration of ecosystems to preserve or re-establish native biodiversity and functions. 3. Prevention is widely promoted as being a more environmentally desirable strategy than actions undertaken after IAS establishment, yet is hindered by the difficulty in separating invasive from non-invasive alien species. Furthermore, the high number of candidate IAS, the investment required in taxonomic support and inspection capacity, and the expense of individual risk assessments may act against the net benefits of prevention. More rewarding avenues may be found by pursuing neural networks to predict the potential composition of pest assemblages in different regions and/or model

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Beyond control : wider implications for the management of biological invasions

https://remotesensing.montana.edu/documents/lawrence_et_al_2006.pdf

Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest)

In this paper, we propose an analysis on the joint effect of hyperspectral and light detection and ranging (LIDAR) data for the classification of complex forest areas. In greater detail, we present: 1) an advanced system for the joint use of hyperspectral and LIDAR data in complex classification problems; 2) an investigation on the effectiveness of the very promising support vector machines (SVMs) and Gaussian maximum likelihood with leave-one-out-covariance algorithm classifiers for the analysis of complex forest scenarios characterized from a high number of species in a multisource framework; and 3) an analysis on the effectiveness of different LIDAR returns and channels (elevation and intensity) for increasing the classification accuracy obtained with hyperspectral images, particularly in relation to the discrimination of very similar classes. Several experiments carried out on a complex forest area in Italy provide interesting conclusions on the effectiveness and potentialities of the joint use of hyperspectral and LIDAR data and on the accuracy of the different classification techniques analyzed in the proposed system. In particular, the elevation channel of the first LIDAR return was very effective for the separation of species with similar spectral signatures but different mean heights, and the SVM classifier proved to be very robust and accurate in the exploitation of the considered multisource data.

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Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas

Conceptually, plant functional types represent a classification scheme between species and broad vegetation types. Historically, these were based on physiological, structural and/or phenological properties, whereas recently, they have reflected plant responses to resources or environmental conditions. Often, an underlying assumption, based on an economic analogy, is that the functional role of vegetation can be identified by linked sets of morphological and physiological traits constrained by resources, based on the hypothesis of functional convergence. Using these concepts, ecologists have defined a variety of functional traits that are often context dependent, and the diversity of proposed traits demonstrates the lack of agreement on universal categories. Historically, remotely sensed data have been interpreted in ways that parallel these observations, often focused on the categorization of vegetation into discrete types, often dependent on the sampling scale. At the same time, current thinking in both ecology and remote sensing has moved towards viewing vegetation as a continuum rather than as discrete classes. The capabilities of new remote sensing instruments have led us to propose a new concept of optically distinguishable functional types ('optical types') as a unique way to address the scale dependence of this problem. This would ensure more direct relationships between ecological information and remote sensing observations.

https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1469-8137.2010.03284.x

Remote sensing of plant functional types

Remote sensing technology is a tool for detecting invasive species affecting forest, rangeland, and pasture environments. This article provides a review of the technology, and algorithms used to process remotely sensed data when detecting weeds and a working example of the detection of spotted knapweed and babysbreath with a hyperspectral sensor. Spotted knapweed and babysbreath frequently invade semiarid rangeland and irrigated pastures of the western United States. Ground surveys to identify the extent of invasive species infestations should be more efficient with the use of classified images from remotely sensed data because dispersal of an invasive plant may have occurred before the discovery or treatment of an infestation. Remote sensing data were classified to determine if infestations of spotted knapweed and babysbreath were detectable in Swan Valley near Idaho Falls, ID. Hyperspectral images at 2-m spatial resolution and 400- to 953-nm spectral resolution with 12-nm increments were used t...

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A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed (Centaurea maculosa) and babysbreath (Gypsophila paniculata) with a hyperspectral sensor

http://www2.geog.ucl.ac.uk/~mdisney/teaching/msc_cons/papers/glenn_et_al.pdf

Hyperspectral data processing for repeat detection of small infestations of leafy spurge

The applicability of high spatial resolution hyperspectral data and small-footprint Light Detection and Ranging (lidar) data to map and describe sagebrush in a semi-arid shrub steppe rangeland is demonstrated. Hyperspectral processing utilized a spectral subset (605 nm to 984 nm) of the reflectance data to classify sagebrush presence to an overall accuracy of 74 percent. With the inclusion of co-registered lidar data, this accuracy increased to 89 percent. Furthermore, lidar data were utilized to generate stand specific descriptive information in areas of sagebrush presence and sagebrush absence. The methods and results of this study lay the framework for utilizing co-registered hyperspectral and lidar data to describe semi-arid shrubs in greater detail than would be feasible using either dataset independently or by most ground based surveys.

/pdf/mapping-sagebrush-distribution-using-fusion-of-hyperspectral-3nwa12pakg.pdf

Mapping Sagebrush Distribution Using Fusion of Hyperspectral and Lidar Classifications

Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques

The Minimum Noise Fraction (MNF) data reduction transform and Mixture Tuned Matched Filtering (MTMF) partial unmixing classification algorithm are relatively new image processing techniques that have proven to be effective target detection tools. These techniques allow partial unmixing and subpixel target abundance estimation, products that cannot be simultaneously achieved using standard mixture modeling or spectral angle mapping algorithms. This paper presents a tangible description of the technical background of both algorithms, a resource that is currently unavailable in existing literature. A demonstration of the use of the MNF and MTMF techniques is presented in detail for application to leafy spurge infestations in the Swan Valley, Idaho. The use of these techniques on hyperspectral imagery generated a producer's accuracy of 63% for infestations with canopy cover averaging 40% for imagery with 3.5 m resolution. Ramifications of image noise estimation and classification endmember selection are discussed at length and should be used as a resource guide for application to other vegetation studies.

/pdf/partial-unmixing-of-hyperspectral-imagery-theory-and-methods-3b7qpyajlw.pdf

Jacob T. Mundt

Papers

A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed (Centaurea maculosa) and babysbreath (Gypsophila paniculata) with a hyperspectral sensor

Hyperspectral data processing for repeat detection of small infestations of leafy spurge

Mapping Sagebrush Distribution Using Fusion of Hyperspectral and Lidar Classifications

Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques

Partial unmixing of hyperspectral imagery: theory and methods