Detecting understory plant invasion in urban forests using LiDAR

Abstract

a b s t r a c tLight detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features -optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense.

Detection of gradients of forest composition in an urban area using imaging spectroscopy

“…Fuentes, Gamon, Qiu, Sims, and Roberts (2001) used AVIRIS-derived pigment and water absorption to map vegetation types in Canadian boreal forests and Kokaly, Despain, Clark, and Livo (2003) mapped forest cover types from the analysis of chlorophyll and leaf water absorption derived from AVIRIS imagery in Yellowstone National Park. In addition, forest structure variables estimated from lidar data such as aboveground/foliar biomass, stand basal area, diameter, height, crown length and crown width (Hawbaker et al, 2010;Lefsky, Cohen, Parker, & Harding, 2002;Lim, Treitz, Baldwin, Morrison, & Green, 2003;Muss, Mladenoff, & Townsend, 2011;Naesset, 2004) have been related to canopy species (Korpela, Orka, Maltamo, Tokola, & Hyyppä, 2010) and understory plant abundance (Singh, Davis, & Meentemeyer, 2015). In this study, we present an approach to map gradients of composition in lieu of composition or specific assemblages as a basis for understanding the functional composition of forests in urban ecosystems.…”

Integrating Modelling and Expert Knowledge for Evaluating Current and Future Scenario of Large Cardamom Crop in Eastern Nepal

“…Other studies have used data from active sensor, mainly LIDAR data, to map understory plants in boreal forests [16,17]. Combinations of LIDAR data and hyperspectral images were used to map understory invasive species in tropical forests [9], and the use of LIDAR data and high-resolution IKONOS imagery to identify understory plant invasion in urban forests [18]. Nevertheless, there are limitations on the use of these data due to its high cost and low availability, mainly in developing countries [19].…”

Remote Sensing in Urban Forestry: Recent Applications and Future Directions

“…While most recent studies agree that the combination of satellite imagery and LiDAR data can lead to higher estimation accuracy, a publication by Singh et al [64] challenges this consensus. The authors found that with the help of a random forest classification method, LiDAR-derived topography metrics could detect understory plant invasion in urban forests with the highest accuracy, exceeding IKONOS data by 17.5% and combined LiDAR and IKONOS data by 5.3%.…”