The Southeastern United States (SEUS) forest comprises 32% of the total U.S. forestland (Oswalt et al. 2014), which combined with the productivity of the forest, places this region at the forefront of American forestry production (Fox et al. 2007). This heterogeneous landscape is composed of heavily managed forests, intensive agriculture, and multiple metropolitan areas. SEUS, although one of the most densely forested regions in the United States (Hanson 2010), is also heavily dissected by road networks (Coffin 2007). The diverse forest management patterns, reflecting long-term land-use legacies (Josephson 1989; Haynes 2002), contribute to the complex land mosaic of SEUS.
It is challenging to quantify the ecological and anthropogenic mechanisms that control the spatial structure of the forest landscape and its surrounding areas in these complex forest mosaics. Forest management is the predominant factor in forest ecology and structural patterns (Becknell et al. 2015), but little is known about how management practices are related to surrounding land-use at the regional scale. One thing that is known is that, in the SEUS, significant expansions of urban areas tend to convert forested land to urban uses and that croplands tend to transition to pine plantations (Davis et al. 2006; Haynes 2002; Wear and Greis 2002, 2012, 2013; Stanturf et al. 2003; Becknell et al. 2015). To understand the ecological and anthropogenic influences of differently managed forests on ecosystem processes, all landscapes should be understood at multiple scales, from the local scale (forest management unit) to regional scales, with the regional scale referring to broad forest mosaics that are formed from management patches (O’Neill et al. 1996). Understanding forest management spatial patterns require defining a map-based management unit, which is the subdivision regarding effects of land use on forest ecosystems. This research seeks to further the understanding of how spatial patterns of forest management affect land use, by posing the following questions: Do roads delineate forest management units? What is the spatial distribution of road-defined forest management units in the SEUS? Moreover, how are the distributions of management units affected by different forest management approaches? And how does forest management affect nearby land use, and how does nearby land use affect forest management?
Forest management is the main driving force of forest structure in SEUS (Becknell et al. 2015) and alters forest properties and processes, which affects forest ecosystem services (Kurz et al. 2008; Stephens et al. 2012; Oswalt et al. 2014). Forest management can be classified into four categories: production forestry, ecological forestry, passive management and preservation management (Becknell et al. 2015). Production management harvests forest products and sustains the bio-productivity of the system with the sole objective of producing wood, pulp, and other forest products. In SEUS, production management based on silviculture systems, which homogenize parts of the landscape, has predominated the SEUS (Siry 2002). However, forest conservation systems have evolved considerably over recent decades (Mitchener and Parker 2005; Franklin et al. 2007). Ecological management uses legacies of disturbance, including intermediate stand disturbance processes such as variable density thinning and fire, and variable and appropriate recovery times to manage forests that still produce economically valuable wood products while preserving many of the values of natural forests (Franklin et al. 2007). Passive management is defined as a practice with little or no active management. We argue that all forests are managed to some degree and that doing nothing is a form of management. Preservation forestry aims to minimize the ecological footprint of society with the objectives of protecting wildlife and maintaining ecosystem services. Furthermore, certain forest management practices can suppress wildfires (Waldrop et al. 1992), prevent insect /pathogen outbreaks (Netherer and Nopp-Mayr 2005; Faccoli and Bernardinelli 2014), change water yield and hydrologic regulation (Douglass 1983), produce wood products, provide places for hunting and recreation, and conserve habitat biodiversity.
Forestland structures, functions, and ecological processes are scale-dependent (Battaglia and Sands 1998; Niemelä 1999; Drever et al. 2006). Regionally, for the purpose of sustainable forest management, we need to develop criteria and indicators of management units. Forest management units in this study are zones or patches, which can be identified, mapped and managed according to the land-use objectives. Road networks link human activities (e.g. management practices) and surrounding physical environments (land cover). For production, preservation, and ecological forestry, in many cases, forest management units are harvest or burn units. Roads are built to create access for the managers and harvesters. For example, the preserved forest in the Ordway Swisher Biological Station (OSBS) in northcentral Florida is subdivided by road networks into management (burn) units, which is the smallest unit of land that is actively managed (Ordway-Swisher Biological Station 2015). In the Joseph W. Jones Ecological Research Center in Ichauway, Georgia, and OSBS, the internal road network provides access to the research site and serves as prescribed fire breaks (Ordway-Swisher Biological Station 2015). For passive management practices, there are currently no clear criteria defining the management units. However, in national forest systems (mostly with ecologically managed forests and multi-use production forests), existing roads and trails are used for controlling prescribed fire and wildfires (e.g. Apalachicola National Forest, Osceola National Forest, United States Department of Agriculture Forest Service 1999). For an example of a privately-owned forest, the Red Hills region in Georgia uses roads to delineate burn units ranging approximately from five to 30 ha (Robertson and Ostertag 2007).
Road networks facilitate movements of humans and connect natural resources with societies and economies. As conduits for human access to nature, the physical footprint of approximately 6.6 million km of roads in the United States FHWA (Federal Highway Administration 2013) has significant primary and secondary impacts on ecosystems and the distribution of species (Bennett 1991). Fifteen to 20 % of American land is subject to the ecological effects of road networks (Forman and Alexander 1998; Forman and Deblinger 2000). The most noticeable effects of road networks on forest structures are landscape structure changes, including reduced mean patch size, increased patch shape complexity, increased edge densities, and reduced unit connectivity. In one case, McGarigal et al. (2001) investigated the landscape structure changes of the San Juan Mountains from 1950 to 1993 and found that roads had a more significant ecological impact (e.g. core forest areas and patch sizes decrease) on landscape structure than logging activities.
In addition to management practices (e.g., harvesting, fertilizing), the construction of road networks divides the forested land into smaller patches, thereby increasing the potential intensity of the effects of management practices. Road networks in managed forests provide easy access for managers and harvesters to extract and regenerate resources (Demir and Hasdemir 2005). Roads may influence fire regimes by increasing fire ignition as a result of human activities (Franklin and Forman 1987). Moreover, road networks alter the spatial configuration of management patches by functioning as firebreaks, which form new patterns in landscapes (Franklin and Forman 1987; Nelson and Finn 1991; Eker and Coban 2010). By quantifying the spatial patterns of management units created by roads we may gain insight into the ecological effects of road networks on spatial forest structures within differently managed areas.
The impacts and ecological effects of roads on the landscape might be misestimated because methods measuring the road-effect zones and landscape scale effects are not yet well developed (Ries et al. 2004; Hou et al. 2013). Roads and streams may be challenging to identify, or invisible because they do not open the canopy so that many roads are not detectable on satellite imagery or even aerial photography. The reliability of large-scale road data is also challenged due to issues of accuracy, coverage and immediacy, all of which can underestimate the extent and ecological impacts of roads on forest structures (Riitters et al. 2004). We propose that common types of forest management are practiced in road-delineated units that are detectable by remote sensing satellite images coupled with crowd-sourced road network datasets. We also describe and study the patterns of forest management units in response to land ownership and different management practices.
Study area
We focus on the Southeastern U.S. Coastal Plain and Piedmont (SEUS) region (Fig. 1). The SEUS is located between Piedmont to the north and the Atlantic Ocean to the east and covers a significant portion of the southeastern United States. The SEUS is the home to the most densely production-forested region nationwide, which makes up 32% of total U. S forest cover (Oswalt et al. 2014). Based on EPA eco-region descriptions, land cover in the SEUS is a mosaic of cropland, pasture, woodland and forests (Bailey 2004). Major silvicultural forests in SEUS are pine forests, such as slash pine (Pinus elliottii Engelm.) and loblolly pine (Pinus taeda L.) forests. European settlement and the extensive harvesting in the early 1900s removed 98% of the original longleaf pine (Pinus palustris Mill.) forests, which was one of the most dominant ecosystems in SEUS (Outcalt 2000) and converted them to plantations of native slash pine. The SEUS forest system is a fire-dominated system with native trees adapted to short-period stand-clearing events. The primary forest management types are production and passive management due to the dominant ownership of private owners, logging companies, and investment institutions (Real Estate Investment Trusts (REITs) and Timber Investment Management Organizations (TIMOs)) (Zhang et al. 2012). This diversity of land cover types is spatially heterogenous, and patch sizes of the numerous vegetation classes vary across a wide range of scales (Fig. 1).
Data sources
Forest extent
In this study, forest extent is determined by a composite of the 2006 and 2011 USGS National Land Cover Database (NLCD), which was constructed from Landsat imagery at 30-m spatial resolution (Jin et al. 2013). We aggregated 21 NLCD classes into two classes: forest (deciduous, evergreen, mixed forest and woody wetland) and non-forest (including water). Only the pixels that contain 50% or more forest area in NLCD will be considered as forested pixels. We also extracted the SEUS urban areas by using the most recent 2015 US Census Bureau’s TIGER cartographic boundary urban areas (TIGER 2015) dataset to remove urban areas from the analysis.
Forest management type
An integrated random forest classifier was built from the analysis of long-term phenological features derived from BFAST outputs and spectral entropy calculated from the Terra-MODIS enhanced vegetation index (EVI: MOD13Q data product), along with ancillary data such as land ownership, and disturbance history to classify different forest management types (Breiman 2001; Verbesselt et al. 2010; Zaccarelli et al. 2013). The forest management type map has a spatial grain of 250 m and is a composite of phenological patterns and changes in the patterns from February 2001 through December 2016 (Figure S1). The SEUS forest management type map has an overall accuracy of 89% for a 10-fold cross-validation. The forest management raster is available for each region as georeferenced GeoTIFF rasters with a 250-m resolution from PANGAEA (Marsik et al. 2017).
Road networks
We selected OpenStreetMap as the primary road data and the USDA National Forest service trail and road maps (https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=transportation, accessed Dec 2016) as secondary in this study. OpenStreetMap is a collaborative, crowdsourced project that creates free, open, and accessible maps of road networks. OpenStreetMap is one of the most popular and well-supported Volunteered Geographic Information (VGI) datasets (Mooney et al. 2010). Community volunteers collect geographic information and submit it to the global OpenStreetMap database (Ciepluch et al. 2009). OpenStreetMap monitors road networks at near real-time and includes additional classes of roads such as private access roads and driveways in rural areas, small service roads or alleys in urban areas, and forest access roads. All those road features are critical for this research and no distinctions were drawn between the types of road, traffic volume, or other factors. OpenStreetMap shows up-to-date road networks information, which the other official road databases do not offer. The accuracy of the OpenStreetMap in our study region has been studied. For the state of Florida, Zielstra and Hochmair (2011) compared road networks dataset from different sources and concluded that OpenStreetMap was significantly better the other road databases. All OpenStreetMap data were downloaded from the website of Geofabrik (http://download.Geofabrik.de, Accessed Dec 2016). USDA National Forest Services Trails and Road Map provide the coverage of detailed transportation map in National Forests (Coghlan and Sowa 1997).
Road density in SEUS was measured as the total length of all roads (in kilometers) in a district divided by the total land coverage area of the district (km2) based on our developed road networks map (Figure S2).
Landscape fire and resource management planning tools (LANDFIRE)
Disturbance data from the Landscape Fire and Resource Management Planning Tools (LANDFIRE) disturbance database were used to evaluate the management unit map. LANDFIRE is a combination of Landsat images, fire program data, and cooperator-provided field data and other ancillary databases (e.g., PAD-US), and is a shared program between the wildland fire management programs of the U.S. Department of Agriculture Forest Services and U.S. Department of the Interior (Rollins 2009). LANDFIRE also describes land cover/use both spatially and temporally from 1999 to 2014 and provides the existing vegetation composition map based on dominant species or group of dominant species. Spatially, LANDFIRE is a Landsat-based (30 m) database, which matches the 30-m spatial resolution of this study (https://www.landfire.gov/disturbance.php). We chose the LANDFIRE project data because the spatial scale is small enough to detect subtle changes brought about by land management practices, and large enough to reflect the characteristic variability of essential ecological processes (such as wildfire) in the appropriate spatial context. The disturbance data from LANDFIRE will be used to evaluate management units delineated by the road network in the context of intensely managed forests in SEUS (Figure S3).
Forest ownership
Geospatial land-ownership data from federal and nongovernmental agencies were integrated for land ownership mapping (Figure S4). Forest ownership in SEUS is broadly categorized as publicly owned and privately owned according to the landowners. There are six sub-types of public ownership, which are federally protected, federal, state protected, state, military, and local. Also, there are four sub-types of private ownership: non-governmental organization, private, family, and corporate. The ownership classification implies different management objectives, as well as landowner skills, budgets and interests. Datasets from other federal and state government agencies were regrouped and classified into ten sub-types to create a comprehensive dataset that includes public land ownership and privately protected easements as well as specially designated areas and associated protection level (see Table S1). The final product is a 250-m spatial resolution raster data depicting the forest ownership types and resampled to 30-m in this study to match the spatial resolution of the NLCD database.