Mapping nonnative plants using hyper spectral imagery Emma Underwood Susan Ustin Deanne Dipietro

Outline Introduction Methodology Results Discussion Conclusions

Introduction Nonnative plants : are plants that have been introduced in to the environment intentionally or accidentally by human activity. Threaten - global diversity - ecosystem functioning Pimentel et al (2000) estimated that 50,000 invasive species have been introduced into the U.S. causing economic losses of $137 billion per year; approximately $35 billion annual cost for plant invasions alone.

The global extent and rapid increase in invasive species is recognized as a primary cause of global biodiversity loss. Nowhere is the ecological threat more clearly seen than in California where more than 1025 plant species have been added to the flora (Rejmánek & Randall, 1994).

Ice plant(Carpobrotus edulis) Common ice plant (Carpobrotus edulis), is an exotic plant species that invades coastal plant communities from the North Coast of California to Mexico. CalEPPC (1999) rated C. edulis as an A-1 species (The Most Invasive Wild land Pest Plant: Widespread).

Jubata grass (Cortaderia jubata) Poses a significant threat to mediterranen ecosystems prolific wind dispersed seeds tolerance of a broad range of habitats and its competitiveness for light,moisture,and nutrients(Cowen,1976).

Key requirement Delineation the spatial extent Severity or intensity of invasion Provides baseline for monitoring future expansion assists in identifying targets for control activities

Offers significant opportunities for providing timely information on invasions of nonnative species into native habitats. Larger spatial area Short period of time Researchers exploit unique phenological, spectral, or structural characteristics of the nonnative species in the image to distinguish them from the mosaic of species around them. Remote sensing-invasive plants mapping

Two ways High spatial,but low spectral resolution-black &white or color infrared aerial photographs Digital images with greater spectral resolution although coarser spatial resolution. Aerial photography - inexpensive, - large amount of archival data - very fine spatial resolution But - relies on nonnative plant possessing visually detectable unique characteristics - Extensive manual labor - Can collect data over a relatively small spatial area

Digital multispectral imagery- automated image processing - large spatial coverage Successful applications of AVHRR, TM and ADAR However, invasive species populations Can be detected only after they become dense and widespread.

AVIRIS (Airborne Visible/infrared imaging spectrometer) Increased spatial resolution Fine spectral resolution 224 spectral bands nm at 10nm resolution 20 m spatial resolution when flying at 11km.

Limitations of using traditionally available wavebands - with color infrared green vegetation is observed as shades of red.

Objective To investigate the use of AVIRIS imagery to detect the invasive species ice plant (Carpobrotus edulis) and Jubata grass(Cortaderia jubata). To compare three techniques for processing the imagery : minimum noise fraction(MNF),Continuum removal,and band ratio indices.

Study site VAFB is used primarily for developing and testing missiles and satellite launches for the Department of Defense and NASA. 836 Vascular plants- quarter of them are invasive species. C.edulis and C.jubata have invaded two native community types :coastal dune scrub community and maritime chaparral.

The focus of this research is the encroachment of ice plant and jubata grass in to these native communities and specifically on the ability of AVIRIS to identify pixels of different densities of these species.

Field work at VAFB Coastal Scrub-iceplant Intact Coastal Scrub Chaparral-iceplant Intact chaparral Pampas-chaparral 5 community types

Methodology Pre-processing - Atmospheric correction - Masks( NDVI > 0.2) Processing - Minimum Noise Fraction - Continuum Removal - Band Ratios Compared processing techniques - Ability to delineate iceplant extent - Detect iceplant density - Assess ease of processing and repeatability

Image Classification  Supervised Classification : requires the user to decide which classes exist in the image, and then These samples known as training areas and are then input into a classification program, which produces a classified image.  Type of supervised Classification:Maximum likelihood classifier Maximum likelihood classification calculates the probability that a given pixel belongs to a specific class.

Unsupervised Classification: when features are separated solely on their spectral properties. Processing Techniques Minimum Noise fraction(MNF) : Often used method for reducing redundancy of information between bands reduce and compress the data increase speed of processing

Continuum removal: is a procedure that facilitates the distinction of similar absorption bands in hyper spectral curves.

Band ratio indices Emphasize the biochemical and the biophysical properties of the vegetation contained in physiologically important bands.

Definitions Confusion matrix : is a square array of numbers set out in rows and columns which express the number of sample units(i.e.,pixels,clusters of pixels etc..) assigned to a particular category relative to the actual category as verified on the ground. Overall accuracy-the total number of correctly classified samples( i.e,the major diagonal) divided by the total number of sample units in the entire matrix.

Producer’s accuracy :is the number of correctly classified samples of a particular category divided by the total number of reference samples from that category. User’s accuracy : is the number of correctly classified samples of a particular category divided by the total number of samples being classified as that category.

Results C omparison of 3 processing techniques

Results Overall accuracy-MNF performed best User’s accuracy-is important User’s accuracy-MNF lowest MNF is able to produce a more accurate map of multiple vegetation classes Continuum removal and band ratio techniques are suited for detecting species with distinct characteristics.

Discussion Evaluated three approaches- accuracy - logistics of processing - ease of interpretation Confusion matrices demonstrated that MNF performed best for classifying all five vegetation communities. –However,in terms of classifying one of the target species,ice plant,the continuum removal and the band ratio methods performed better.

Key findings.. MNF Worked well identifying different densities of ice plant X Difficult to interpret Band Ratio Intuitive Continuum Removal Good results for presence / absence of ice plant Easy to use X Speckled result, accuracy not improved when lumped

Conclusions The benefit of this research has been to contribute to the knowledge base of land managers at VAFB by providing improved information on the spatial extent and the density of the ice plant and jubata grass, which will lead to better protection of the native biodiversity.