Investigating Methods In Remote Sensing For Invasive Plant Species - PowerPoint PPT Presentation

Investigating Methods In Remote Sensing For Invasive Plant Species Identification FRST 443 Alyssia Law, Bryana Ginther, Joyce Chan, Agatha Czekajlo

Invasive Species: Who Cares? 1st largest threat to biodiversity: habitat loss ● 2nd largest threat to biodiversity: INVASIVE SPECIES ● Other factors negatively impacted by invasive species: ● ○ Economy Primary productivity ○ Regional Hydrology ○ …. ○ English holly. Personal photo Himalayan Blackberry. Retrieved from: Invasive Species Council of BC

Why Use Remote Sensing to Identify Invasive Species? Invasive species are incredibly ● widespread Detailed field surveying = $$$! ● Remote sensing can delineate ● invasive species patches Areas most at risk can then be ● identified and removal teams can be sent in

Outline 1. Current technologies and methodologies used to identify invasive species 2. Remote sensing case studies of invasive species present in British Columbia 3. Future direction of research

How are invasive species identified? 1. Multispectral Imaging (Landsat) Advantages - Accessible, cost effective - Successful in mapping large dense regions of invasive species Disadvantages Less spectral bands, often lower spatial resolution - Unsuccessful at mapping less dense regions of invasive species -

How are invasive species identified? 2. Hyperspectral Imagery (Examples: AVIRIS, SPOT) A dvantages - High spatial resolution - Hundreds of spectral bands - Phenological differences 1. Varying chlorophyll 2. Water absorption 3. Nitrogen levels Disadvantages - Background knowledge on habitat required (Asner and Vitousek, 2005)

How are invasive species identified? 3. LiDAR Advantages - Detects intermediate layers - Maps canopy structure - Orographic predictors - Forest height strata Disadvantages - Extensive calibration - Difficult to distinguish species (Singh et al., 2015)

Classification by Computer Algorithm Models Machine-learning classification algorithms: ● Random Forest ○ Support Vector Machine (SVM) ○ Regression models ● Weed Invasions Susceptibility Prediction ● (WISP) Uses GIS to determine areas, and the ○ degree of, invasion susceptibility

Hunt et al. 2007 1. Focus: Detection of leafy spurge ( Euphorbia esula ) among other vegetation Yellow-green flower bracts a. Flowers in early June to mid-July b. 2. Methods 20 plots with varying levels of leafy spurge a. Sensors: AVIR IS, Landsat 7 ETM+, SPOT 4 b. Leafy spurge. Retrieved from https://gobotany.newenglandwild.org/species/ euphorbia/esula/

Hunt et al. 2007 1. Classification a. Spectral Angle Mapper classification 2. Results a. Hyperspectral AVIRIS (48 bands) i. Overall accuracy = 74% b. Multispectral AVIRIS (7 bands in visible + near infrared) i. Overall accuracy = 56% c. Landsat 7 ETM+ (bands 1-5, 7) and SPOT 4 (bands 1-4) i. Overall accuracy insignificant

Hunt et al. 2007 1. Using only visible and near infrared bands a. Landsat 7 ETM+ (bands 1-4) i. Overall accuracy = 59% b. SPOT 4 (bands 1-3) i. Overall accuracy = 61% 2. Takeaways from the study a. Higher spectral resolution images = higher accuracy b. Bands in visible/near infrared wavelengths = higher accuracy

Hill et al. 2016 1. Focus: Mapping of Scotch broom ( Cytisus scoparius ) using Landsat a. Yellow flowers b. Flowering in June - July 2. Methods a. Sensor: Landsat Thematic Mapper b. 5 images over 4 years c. Aerial observation to find areas of known concentration i. Dense (≥ 75% cover) ii. Moderate (25 - 75% cover) iii. Low (10 - 25% cover)

Hill et al. 2016 1. Results a. Around 80% (moderate or dense patches) b. Results unreliable for low density c. Often entirely missed patches that were < 0.5 ha

Hill et al. 2016 1. Takeaways from the study a. Landsat is useful for mapping Scotch broom during high flowering 2. Issues a. Low density patches (early detection)

Chance et al., 2016 Objective: detect invasions of Himalayan blackberry and English ivy in urban ● ecosystems (Surrey, BC). Chance et al., 2016.

Chance et al., 2016 RESULTS Himalayan Blackberry accuracy: 77.8 ● to 87.8% ● English Ivy accuracy: 81.9 to 82.1 % Presence of species decreased with ● distance from roads. Chance et al., 2016.

Future direction Increase time scale ● Multiple growth/flowering seasons ○ Over the year across seasons ○ ● More collaborations between ecologists and RS scientists Ecologists → identify invader-specific plant traits and abiotic tolerances ○ RS Scientists → develop robust RS predictors to identify ecological & environmental markers ○ for detection

Future direction Co-development of sensors with ecophysiology ground ● measurements ○ Ex. active imaging with LiDAR or RADAR, fine resolution hyperspectral and hyperspatial tech ‘Trait products’ on future satellites with spectrometer–LiDAR systems for ● invasive monitoring Ex. Imaging spectroscopy of chemical and morphological traits across broad regions ○

Summary of Main Points ● Best RS method of identifying invasive species: combine LiDAR and Hyperspectral imagery ● Find unique features to detect, such as flower colour ● Ecologists and RS scientists should co-develop RS technology

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