Quantifying the spatial and temporal distribution of tanglehead (Heteropogon contortus) on South Texas rangelands

Abstract

Introduction: Tanglehead is a grass native to southwestern US rangelands; however, its prevalence as a native invasive on South Texas rangelands has increased rapidly during the last decade. Large areas of monotypic stands have emerged in Jim Hogg, Duval, Brooks, and Kleberg counties. The aim of this research is to understand the spatial and temporal dynamics of these invasions as a model for the assessment of native invasive species. Our specific objectives were to (1) evaluate the feasibility of classifying tanglehead using 1-m resolution imagery data, (2) assess the spatial and temporal distribution of tanglehead in relation to soil type and distance from roads, and (3) quantify the temporal and spatial distribution of tanglehead on our study sites. We combined remote sensing approaches with landscape metrics to quantify the spatial and temporal distribution of tanglehead in five locations across our study area. We calculated the normalized difference vegetation index and combined it with the original aerial imagery to conduct an unsupervised classification with the following land cover classes: woody vegetation, tanglehead, non-tanglehead herbaceous, and bare ground. Soil type and the distance from roads were assessed to determine the relationship between these factors and tanglehead spatial distribution. Results: We were able to successfully map tanglehead using the 1-m imagery. Our image classification approach resulted in accuracies greater than 85% for all sites. Tanglehead occurred in sandy, loamy sand, and sandy loam soils. Over 70% of tanglehead cover occurred within the first 150 m from the edge of roads. This cover increased from 7.1% (SE = 1.1%) in 2008 to 17.8% (SE = 5.4%) in 2014. Once established, small patches of tanglehead began aggregating or coalescing with existing stands, thereby creating larger patches over larger areas. Conclusions: Our study has shown the value of analyzing spatiotemporal dynamics of tanglehead with remote sensing techniques and landscape metrics to improve our understanding of establishment and dispersal processes of a native invasive. This study provides useful information to improve rangeland management decisions as well as assessing native invasive dynamics with potential applications for assessing its effects on wildlife habitat, livestock operations, and habitat restoration strategies.

Co‐occurrence of bobcats, coyotes, and ocelots in Texas

“…We quantified landscape‐ or patch‐level metrics we believe likely influenced seasonal co‐occurrence patterns. To examine whether the spatial structure of woody vegetation influenced seasonal co‐occurrence patterns, we conducted a 1‐m land cover classification of the study area using 2014, 1‐m National Agriculture Imagery Program Digital Orthophoto Quarter Quadrangles (Texas Natural Resources System) in ERDAS IMAGINE (Hexagon Geospatial) based on four broad habitat categories: herbaceous (i.e., coastal prairie, herbaceous emergent wetlands, grasslands), water (i.e., lagunas and anthropogenic waterways), bare ground (inland dunes, caliche roads, and Texas Farm‐to‐Market 186 [paved road]), and woody cover (thornscrub, mesquite, and live oak forests and mottes) (Jensen, 2016; Mata et al, 2018). Using a Trimble ® Geo 7 Series Handheld Computer with 1 m precision or a Trimble Nomad ® 1050 Series Handheld Computer with GBSS 1 m precision (Trimble Navigation, Ltd), we collected 629 ground‐truth points collected in June and September 2016.…”

Land Cover Trends in South Texas (1987–2050): Potential Implications for Wild Felids

“…An accuracy assessment was conducted in ArcMap 10.5.1 for each year analyzed based on 200 random points, where each image was considered satisfactory once accuracy reached 85% [22,36]. We first conducted an accuracy assessment with the 2016 image and verified it against knowledge of the landscape based on concurrent fieldwork in the region, high-resolution aerial imagery, and Google Maps following similar recently developed approaches [39,40]. Once we were able to identify the different categories in 2016, we used the same approach for the previous year and we used Google Earth, National Agriculture Imagery Program historical imagery to evaluate land cover in the study area.…”

Invasive grasses in South Texas rangelands: historical perspectives and future directions

“…Using higher spatial resolutions (<5-m pixel size) than MODIS and Landsat, aerial photography has also been used to detect the phenological responses of P. ciliare to water stress in South Texas (Everitt et al 1987). More recently, Mata et al (2018) classified National Agriculture Imagery Program images to assess the spatial and temporal distribution of H. contortus (Figure 8) between 2008 and 2014 and observed an increase from 4% to 20% in H. contortus cover. Although aerial photography provides higher spatial resolution for lower cost, it lacks the spectral resolution of satellite imagery, usually providing only bands in the visible and near-infrared spectra (Huang and Asner 2009;Underwood et al 2003).…”

“…While remote sensing has advanced significantly in terms of platforms, classification algorithms, and access to data and classification approaches through the cloud, there is a need to improve accuracy and the delivery of information to end users (e.g., ranchers, land managers). Traditional image classifications have allowed the classification of invasive monocultures of H. contortus in South Texas with accuracies greater than 85% (Mata et al 2018). However, there is a need to develop classification approaches for the other species in South Texas.…”

“…New approaches such as deep learning and artificial intelligence can potentially improve accuracies to greater than 95% (Zeng et al 2019). The use of multiple periods and time-series analyses can be used to inform the temporal dynamics of invasive grasses at local and regional scales (Mata et al 2018;Villarreal et al 2019). The use of landscape metrics to assess patch dynamics can be used to evaluate the spread of invasive grasses across the landscape (Mata et al 2018).…”