2. Introduction
The importance of forest and landscape changes are well-documented in the ecology and land use literature. To support natural resource management and strategic forest planning pursuant to the United States Resources Planning Act (RPA; P.L. 93-378, 88 Stat 475, as amended), the objective of this study was to examine forest change in the continental United States (CONUS) from 2001 to 2016 in relation to the dynamic landscape context within which the forest changes occurred. Forest change is common throughout the forests of North America. Much of the evidence of recent forest change comes from temporal analyses of satellite images, primarily by evaluating vegetation indices in terms of stand-clearing events. Masek et al. [
1] reported a loss of 0.9% of forest area per year from 1990 to 2000, with local rates approaching 3% per year. Hansen et al. [
2] estimated the mean annual forest loss from 2000 to 2005 was ~50,000 km
2 per year, which is equivalent to an annual loss of ~0.9%. In the CONUS, Schleeweis et al. [
3] found that similar rates of forest disturbance were sustained over a longer time period during 1986–2020. While climate change likely plays a role in forest loss [
4], pervasive forestry operations appear to be the primary driver of forest change over most of the CONUS, with secondary drivers (e.g., insects, diseases, fire) affecting an increasing area of forest over time [
1,
3,
5,
6]. Forest loss due to land use conversion from forest to agriculture or urban land persists longer than other forest area changes, and although such conversions are smaller in total area, they are also pervasive and locally important where they occur [
6,
7].
Despite the evidence of large forest losses, the total CONUS “forestland” area (i.e., the area that is “used” as forest) has been stable for several decades [
8,
9]. These seemingly disparate findings are reconciled by Coulston et al. [
10], who noted that forest land use does not imply continuous forest cover, and that net change of forest cover also depends on forest cover gain which balances forest cover loss. Supporting both perspectives are CONUS land cover data from 2001 to 2016 which show that the large area of forest cover loss approximated the area gained, and that most of the transitions from (or to) forest cover were typically to (or from) shrub and grass forest cover [
7]. Those types of transitions are consistent with the attribution of forest loss to forestry operations because grass and shrub covers are transitional, early-succession land covers that accompany natural or artificial forest regeneration [
9].
Both net and gross forest area changes have additional shortcomings as indicators of forest change because they ignore not only the spatial pattern of change but also the landscape context (or “setting”) within which the change occurs. The fact that forest is both lost and gained during a specified interval implies that the spatial distribution of the forest cover must change over that interval, even if net forest area remains the same. The spatial patterns of forest change are not necessarily uniform with respect to landscape context, and the rate of change varies substantially among different types of landscapes even as the landscapes themselves change over time [
11]. Especially important is the appearance or expansion of intensive land uses such as agriculture and infrastructure (e.g., buildings, roads) near forest areas, which sets up new ecological risks, societal concerns, and management challenges for forests. That is so because the landscape context partly determines the specific effects of a forest change, including the value that society places on that event, and it constrains forest management options and efficacy. For example, a wildfire in a suburban area has a very different set of ecological and social-economic consequences when compared to a fire in a wilderness area, and in both settings fire management options are constrained by the transportation infrastructure, and restoration efficacy depends on what type of forest is restored [
12]. Similarly, invasive species management must consider landscape context because nearby intensive land uses increase forest invasibility, introduce exotic species, and constrain management options [
13,
14,
15,
16]. Local forest managers have long considered landscape context, as evidenced by forested stream buffers [
17], visual screens along roadways [
18], and the location of forest-based recreation experiences [
19]. There is now a need for regional assessments to also consider the geography of forest change, as it relates to the evolving landscape context within which the change occurs.
A notable example in the United States is the recent focus on the “wildland-urban interface” which arose in response to concerns about water quality and wildfire [
20]. From a broader perspective, however, the expansion of the wildland-urban interface is only a part of the total picture of landscape change. Urban expansion also encroaches upon adjacent agricultural landscapes which contain forest, and in agriculture-urban interfaces the forest may be valued mainly for its rarity. In contrast, the dynamics of forest within agricultural settings depend primarily on the relative economic benefit of tree crops versus other crops [
21] and incentives such as the Conservation Reserve Program [
22]. An even broader perspective recognizes the goal of “keeping forests as forest” [
23] which implies maintenance of forest-dominated landscapes even as expanding agriculture and urban land uses compete with forest land use and form interface areas within those landscapes. Put simply, forest cannot be sustained unless forested landscapes are sustained [
11]. Even casual observation shows that continued expansion of an urban (or agriculture) interface within a forested landscape leads to a landscape that is dominated by developed (or agricultural) land and that the remnant forest exists within a novel landscape context. From a systems theoretic perspective, such changes in landscape “dominance” have social-ecological implications that are more fundamental than those arising in landscape “interfaces” [
24].
Qualitative concepts like landscape context and setting have been quantified for regional forest assessments [
25,
26]. This study was designed to improve the 2020 CONUS RPA assessment by extending landscape analyses to consider forest area dynamics in relation to landscape change over time. We characterized overall landscape change and then refined the analysis to examine forest change within the changing landscapes. Our conceptual model has three parts to consider land cover change, exposure to nearby change processes, and the landscape context of the exposure and change. Both conceptually and in practice, changes to and from forest cover occur at specific locations, such that transition matrices prepared from land cover maps provide an appropriate model for analyses. We implemented analyses of forest cover change using the National Land Cover Database (NLCD) [
7]. We also conceptualized the exposure of a given location to a nearby change in terms of the edge effects of that change, which typically permeates the immediate neighborhood of the change [
27,
28]. We evaluated five types of exposures in the neighborhood of each location. Three types of forest change (forest removal, forest fire, and forest stress) were identified using the North American Forest Dynamics–Attribution (NAFD-ATT) disturbance attribution map [
3], and two types of land use change (increased agriculture or developed cover) were identified from the NLCD land cover maps. More than one change process can (and often does) occur in the same neighborhood, for example where forest removal is followed by increased developed cover. For simplicity, we ignored interactions among change processes and analyzed each process type separately. The conceptual model of landscape context emphasizes the importance of intensive human land uses (i.e., agriculture and development), which have been the primary drivers of global forest area change for centuries [
29]. We implemented that concept by using the landscape mosaic classification [
30], which is an indicator of overall ecological condition [
31] that also quantifies landscape dominance and interfaces [
32]. Each location was described by the proportions of agriculture and developed land covers in relation to all other land cover within a surrounding neighborhood. For consistency with forest cover change data, we implemented the landscape mosaic classification using the same NLCD land cover data.
The first objective of our investigation was to examine overall changes in the landscape mosaic, dominance, and interface in the continental United States from 2001 to 2016. The second objective was to characterize the exposure to change processes that occurred within each landscape mosaic from 2001 to 2010, and to evaluate how much of each mosaic class area was exposed to those processes. Our third objective was to examine forest change in relation to changes of the landscape context including to the landscape mosaic, dominance, and interface from 2001 to 2016. We discuss the results in terms of evidence supporting whether forest change was temporary versus permanent, and natural versus anthropogenic, and we suggest applications of our approach for mapping, monitoring, and modeling landscape and land use change.
6. Discussion
The geography of change matters, and land managers and planners need to know the landscape context within which changes occurred or would be expected. In this study of landscape changes from 2001 to 2016, land cover changes resulted in a change of landscape mosaic for an area equivalent to 5.3% of the total area of the continental United States (excluding water area). Whether measured by forest-nonforest transitions or by the shifting landscape mosaic of persistent forest, forest dynamics within those landscapes was a major contributor to overall change. From 2001 to 2016, the forest area decreased by 2.6%, but the total gross change (11.0%) was more than four times larger than net change. An additional 3.2% of the persistent forest experienced a change of landscape mosaic. Thus, landscape mosaic change affected an area equivalent to 29% of the total gross change and increased the overall rate of change from 11.0% to 14.2% of the total forest area in 2001. To put those numbers in perspective, the equivalent average annual rate of change (~0.9%) is roughly the same as the annual North America forest loss rates [
1,
2].
The nature of land cover data makes it difficult to use that data alone to establish whether the forest change was temporary versus permanent, or natural versus anthropogenic, but information about the landscape context of forest change provides some of the best evidence to date that most of the change was temporary. The land cover data alone (
Table 1) support the conclusion [
1] that most forest canopy cover change is temporary and due to pervasive forestry operations. Of the 161,733 km
2 of gross forest cover loss, 87% was converted to shrub or grass cover, and 75% of the gross forest cover gain was conversion from shrub or grass covers which typically appear after forestry operations. The contextual information provided by the landscape mosaic classification strengthens that evidence by showing that most forest loss and gain occurs in landscapes containing relatively little agriculture or developed land. However, we also found evidence that a substantial area of forest loss was probably permanent simply because it occurred in developed landscapes, and that more forest area was assimilated into newly developed landscapes where the likelihood of future forest loss is higher. If present trends of increases in human population and development continue, then there will be more permanent conversions and thus less forest area available to support not only forestry operations but also all other forest-based amenities such as water and recreation [
25]. Over the long term, the sustainability of all forest amenities depends on sustaining the forested landscapes within which the forest can be used as forest.
Our results concerning exposure to forest change processes are generally consistent with earlier studies in that change was a common event, and forest removal was the most important type of change [
1,
3,
5]. With one exception, we found that exposure to fire and stress was common only in natural landscapes. We attribute the exception (mosaic Nd) mosaic to events occurring near isolated roads (a type of developed land cover) in mostly forested landscapes [
38]. The relatively large exposures to forest removal in many of the mosaic classes that were not dominated by natural cover are explained by noting that expansion of agriculture and developed land area often requires forest removal. We found that exposure to increased agriculture or developed land uses occurred primarily in landscapes that were already agricultural or developed landscapes, lending additional support to the argument that intensified land use is not a major component of forest dynamics in remote areas.
The extensive human “footprint” may consider many types of human activities [
39,
40]. The landscape mosaic classification can be viewed as the component of the human footprint which identifies the signals of intensive land uses near otherwise natural areas. Typical investigations of such interface areas have focused on the expansion of developed area and the wildland-urban interface where trees co-occur with houses [
41,
42]. Our results for expansion of the developed interface class are generally consistent with those investigations, but we also found evidence of urban expansion into agricultural landscapes where trees also occur. Several aspects of our approach could be useful in future studies of various types of interface. Unlike current procedures for mapping wildland-urban interface [
42], the mosaic is mapped at a fixed (and higher) spatial resolution (equal to the resolution of the land cover data), stationarity need not be assumed within the housing density regions which define the wildland-urban interface classes, and the modifiable area unit problem [
43] is postponed until after the mosaics are mapped. With landscape mosaics as a basis, the housing density thresholds used to define the wildland-urban interface (or other aspects of the human footprint) can be incorporated later as a refinement of the landscape mosaic map. Use of the same landscape mosaic classification for different refinements permits, for example, consistent analysis of the wildland-urban, agriculture-urban, and wildland-agriculture interfaces, which would be important when investigating land use impacts on broad-scale spatial processes such as pollination or the spread of invasive species. Even without further analysis, temporal animations of the shifting landscape mosaic help to visualize the type, location, scale, and velocity of changes in the human footprint (see
Supplemental Videos).
This study addressed questions about the causes and consequences of forest and landscape pattern changes at regional scale. The synoptic data we used are essential for assessing landscape patterns consistently over such a large area and are a first step to guide broad policy [
24]. A synoptic assessment helps to refine the scope of future research by identifying where some questions should be asked in more detail, and why the investigation is important in terms of local societal values and resource management opportunities. Predicting and managing the local impacts of change will always depend on circumstances such as the specific landscape history [
44], the cause of the change [
45], and the specific question or species that is being addressed [
46]. However, such detailed investigations typically require more detailed spatial and thematic data that are either unavailable or inconsistent over large regions. One can argue the relative merits of up-scaling versus down-scaling approaches to address a given question across scales, but as a practical matter neither approach can stand alone if cross-disciplinary integration is envisioned. For that purpose, we believe the landscape mosaic classification can provide a consistent synoptic and local framework for spatial analysis of land use change, one which can accommodate a variety of disciplinary perspectives and investigations.
We can suggest several paths for future research and applications of landscape mosaics. Our focal class was forest, but the same approach could be used to explore change of agriculture land which is another important resource that is threatened by development [
47,
48]. Alternatively, point (or field plot) data could be attributed with mosaic classes to explore temporal changes of that data in relation to landscape context [
49]. The demonstrated dependence of forest change on landscape mosaic suggests the use of landscape mosaics as independent variables in models of future forest change. Alternatively, the “spatial credibility” of predicted land use changes could be validated by comparing historical evolutions with the predicted future evolution of the landscape mosaic. Markov chain models of landscape mosaic change may be useful when there are no other predictor data [
50]. For that application, the 18 × 18 transition matrix that we used to summarize forest change simply adds a mosaic label to the forest states in a traditional 2 × 2 (forest-nonforest) transition matrix. Using methods and terminology from Hill et al. [
51], the nonforest state is “substrate,” and a Markov chain analysis of mosaic “community dynamics” provides many descriptors of landscape and forest dynamics. For example, relative convergence rates towards steady state distributions of landscape mosaics alone and forest within landscape mosaics indicate the sustainability of forest area within a shrinking area of forested landscapes [
11]. We specified a mosaic neighborhood size that is relevant to understanding local land use changes in the United States. However, the landscape mosaic is scale-dependent, and the neighborhood size is simply a tuning parameter. Larger (or smaller) neighborhood sizes will characterize lower (or higher) spatial frequencies of land cover variance (
Supplemental Figure S4). Following a multi-scale analysis, a “scale domain” at a given location is a range of neighborhood size over which the mosaic class is invariant. Discontinuities on a map of scale domains could represent locations of mosaic “tipping points” and indicate their spatial scales. In principle, a ternary classification can be applied to any categorical map, and software is available to use other data and threshold values [
52]. Our research demonstrates that it is feasible and informative to examine forest change within a changing landscape context at a high spatial resolution and at a continental extent.