Next Article in Journal
Object-Based Analysis of Airborne LiDAR Data for Building Change Detection
Next Article in Special Issue
Integrated Geophysical and Aerial Sensing Methods for Archaeology: A Case History in the Punic Site of Villamar (Sardinia, Italy)
Previous Article in Journal
An EOF-Based Algorithm to Estimate Chlorophyll a Concentrations in Taihu Lake from MODIS Land-Band Measurements: Implications for Near Real-Time Applications and Forecasting Models
Previous Article in Special Issue
Tridimensional Reconstruction Applied to Cultural Heritage with the Use of Camera-Equipped UAV and Terrestrial Laser Scanner
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 6
Issue 11
10.3390/rs61110716
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Quantifying Ancient Maya Land Use Legacy Effects on Contemporary Rainforest Canopy Structure

by
Jessica N. Hightower
1,2,
A. Christine Butterfield
1 and
John F. Weishampel
1,*
1
Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL 32816, USA
2
Round River Conservation Studies, 284 West 400 North #105, Salt Lake City, UT 84103, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(11), 10716-10732; https://doi.org/10.3390/rs61110716
Submission received: 1 July 2014 / Revised: 27 October 2014 / Accepted: 27 October 2014 / Published: 6 November 2014
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
Browse Figures
Versions Notes

Abstract

:
Human land use legacies have significant and long-lasting ecological impacts across landscapes. Investigating ancient (>400 years) legacy effects can be problematic due to the difficulty in detecting specific, historic land uses, especially those hidden beneath dense canopies. Caracol, the largest (~200 km2) Maya archaeological site in Belize, was abandoned ca. A.D. 900, leaving behind myriad structures, causeways, and an extensive network of agricultural terraces that persist beneath the architecturally complex tropical forest canopy. Airborne LiDAR enables the detection of these below-canopy archaeological features while simultaneously providing a detailed record of the aboveground 3-dimensional canopy organization, which is indicative of a forest’s ecological function. Here, this remote sensing technology is used to determine the effects of ancient land use legacies on contemporary forest structure. Canopy morphology was assessed by extracting LiDAR point clouds (0.25 ha plots) from LiDAR-identified terraced (n = 150) and non-terraced (n = 150) areas on low (0°–10°), medium (10°–20°), and high (>20°) slopes. We calculated the average canopy height, canopy openness, and vertical diversity from the LiDAR returns, with topographic features (i.e., slope, elevation, and aspect) as covariates. Using a PerMANOVA procedure, we determined that forests growing on agricultural terraces exhibited significantly different canopy structure from those growing on non-terraced land. Terraces appear to mediate the effect of slope, resulting in less structural variation between slope and non-sloped land and yielding taller, more closed, more vertically diverse forests. These human land uses abandoned >1000 years ago continue to impact contemporary tropical rainforests having implications related to arboreal habitat and carbon storage.

Graphical Abstract

1. Background and Rationale

1.1. Human Land Use Legacies

One interface between the disciplines of landscape archaeology and landscape ecology is the study of land use legacies. Understanding the consequences of human alteration of landscapes has become increasingly important as human populations continue to expand and natural landscapes are continually transformed [1,2]. Just as modern human societies alter landscapes, so did prior ones [3]. Land use history (e.g., forest clearing, agricultural regime, abandonment) directly influences both the biotic (e.g., presence of novel species assemblages) and abiotic (e.g., changes in soil nutrients, hydrology, topography) environments [4]. These changes may lead to long-term effects on contemporary measures of biodiversity (i.e., composition, structure, and function) that may impact current services (e.g., habitat, carbon sequestration) provided by the ecosystem (Figure 1).
Remotesensing 06 10716 g001 1024
Figure 1. The interacting and cascading effects of land use legacies on modern forested systems adapted from [4]. Regions enclosed by dashed outlines include some parameters that can be measured with airborne LiDAR. Blue and red outlines represent historic-archaeological (past) influences and contemporary (present) factors, respectively.
Figure 1. The interacting and cascading effects of land use legacies on modern forested systems adapted from [4]. Regions enclosed by dashed outlines include some parameters that can be measured with airborne LiDAR. Blue and red outlines represent historic-archaeological (past) influences and contemporary (present) factors, respectively.
Remotesensing 06 10716 g001
While deforestation is still occurring at an alarming rate throughout many parts of the world, afforestation is occurring in other regions as former agricultural fields are abandoned [5,6,7]. By one estimate 50%–80% of the forests in New England are on former agricultural fields [8]. Locations that have traditionally been thought of as sparsely populated and fairly pristine prior to European colonization, such as the Americas, are now known to have been significantly altered by ancient humans [9,10]; hence, the concept of “virgin” forests is now relatively obsolete [11,12]. Evidence suggests that vast tracks of forest had been cleared for agriculture or burned by indigenous humans prior to western arrival in the New World [13,14,15]. Given the current rate of land alteration and the extensiveness of past alteration by ancient people, the persistence and implications of these land use legacies have become important questions in ecology [16,17,18].
The time since a land use has been abandoned and allowed to revert back to a natural state is a determinant of the type and extent of the legacy present [1,2,10]. In European forests, agricultural legacies have been shown to endure over a millennium after abandonment, with species composition in secondary forests that regenerated over former agricultural fields lacking native species found in ancient forests [13,19]. Another example of ancient land use legacies continuing today is found in Central America with the Maya civilization. A large percentage of forests in Central America are secondary forests that have regenerated after the collapse of the Maya civilization [10,20]. Studies have suggested these forests contain trees species that were of economic importance to the ancient Maya, which may persist as remnants of the Maya orchard-gardens [21]. In addition to species introduction by the Maya, disturbance from agriculture and settlement constructions may have altered the landscape in ways that provided optimal habitat for specific, limestone loving species, such as the Ramon tree (Brosimum alicastrum) [22,23]. Fingerprints of ancient land use may also be found in forest structure (basal area, biomass, canopy height canopy closure, etc.) independent of species composition [24,25]. Forests may take between 60–200 years to regenerate to their previous canopy height [26,27]. However, structure has been shown to be influenced by topography [28] and soil nutrients [29], which may have been irrevocably altered by past land use, making it impossible for forests to regenerate to pre-land alteration conditions [2,17].

1.2. Forest Canopy Structure

The structure of a forest offers key insights into ecosystem function and biodiversity. It has been directly correlated to a number of important ecological measures, such as biodiversity and carbon stocks, i.e., aboveground biomass (AGB) [30,31]. By describing spatial patterns we can determine processes at work in a landscape [32]. Forest structure can be directly tied to species diversity, with a more heterogeneous structure often indicative of not only higher floral diversity, but also high faunal diversity as well [33,34,35]. The structure of forests is often used as a proxy for determining live AGB, an important factor in identifying sources of carbon sequestration [28,36]. Forest structure relates to the health of an ecosystem, where a higher degree of heterogeneity is suggestive of a more dynamic forest (i.e., disturbance, gaps, recruitment) [37,38]. Understanding the factors that drive forest structure can enable a better understanding of general ecosystem function [39,40,41].
A number of factors are known to affect forest structure, among them natural and human disturbance [42], climate, soil type and nutrients, and topographic position [29,43]. Topographic position, which include measures of relief (or slope) and elevation, have been shown to have a strong effect on forest structure. Forest canopy height and AGB have been directly correlated to topographic relief, with high AGB and greater canopy heights in lower slopes and valleys compared to steeper slopes which are typically associated with lower AGB and lower canopy heights [28,44]. Canopy openness has also been shown to increase on steeper slopes compared to low slopes and valleys [45]. While topographic position appears to be a strong factor in influencing forest structure, another important, if indirect, factor to consider is disturbance.
While natural disturbances such as floods, fire, and wind damage can drastically alter the structure of a forest [30,46], human disturbances can also have a significant impact [24,25,47]. However, for many human land uses, regenerating forests attain levels of vertical structure very close to original levels within 50 to 200 years [25,27]. Other forms of human land use may have longer lasting impacts, depending on the intensity of former land use [2]. Land use that alters the topography of the landscape has the potential to permanently alter the structure of the forest that regenerates, so long as the change in topography remains. One such land use can be found at Caracol, Belize, where the Maya extensively terraced across the hilly landscape. The terraces were constructed between A.D. 650–900 and are still in place today [48,49]. The terracing has essentially created fine-scale, level areas along hill slopes, effectively transforming the micro-topography of the region. Thus, Caracol provides the opportunity to investigate the extent to which human-altered topography continues to impact the ecosystem 1000 years after forest reestablishment.

2. Objectives

Though archaeologists typically wish to ignore or remove the vegetation that obscures the view of their ancient landscapes [50] and ecologists often wish to discount the influence of historic events on ecosystem composition, structure, and function [2], this study exploits the ability of LiDAR to simultaneously map forest canopy and below-canopy archaeological surfaces. Specifically, the goals of this research are to use LiDAR to (1) delineate the location of Maya agricultural terracing and areas where there are no detectable terraces and (2) quantify structural attributes of the forest canopy (i.e., height, openness, and vertical diversity) at Caracol. Furnished with this information, we can (3) assess the extent to which the ancient Maya agriculturally engineered (i.e., terraced) landscape impacts the LiDAR-derived forest structure of today.

3. Study Area

The Caracol Archaeological Reserve is located in west-central Belize along the Guatemalan border. The reserve resides across a karst landscape consisting of alternating hills and valleys, with elevations ranging between ~310 to 720 m asl. The Caracol study site encompasses an area of ~200 km2 (Figure 2) and is surrounded by the Chiquibil National Park. It represents a massive Maya city with extensive monumental architecture whose extent has been documented through LiDAR [51,52]. The dense subtropical moist forest of Caracol reaches average heights of 22 m and leaf off never exceeds 20%, with lowest leaf area index occurring during the end of the dry season (end of March, beginning of April) [42]. Recent human and natural disturbance of the forest has included hurricanes in 1968 and 1973 [53] and encroachment from Guatemala. These incursions entail the clearing of large swaths of forest along the border and targeted removal of desirable tree species throughout the reserve, primarily Swietenia macrophylla (Mahogany) and Cedrela odorata (Cedar) [54].
Remotesensing 06 10716 g002 1024
Figure 2. Hillshaded DEM of the Caracol study site with 300 sample plot locations randomly distributed across terraced and non-terraced areas. Samples, which were stratified by land use type (terrace and non-terrace) and by slope (low, medium, high), were not collected near the Guatemala border, where there is ongoing deforestation (shown in pink).
Figure 2. Hillshaded DEM of the Caracol study site with 300 sample plot locations randomly distributed across terraced and non-terraced areas. Samples, which were stratified by land use type (terrace and non-terrace) and by slope (low, medium, high), were not collected near the Guatemala border, where there is ongoing deforestation (shown in pink).
Remotesensing 06 10716 g002
Ancient Maya land use is ubiquitous throughout the study site. Thousands of residential and ceremonial structures are found throughout the ~200 km2 area, as well as ~67 km of causeways radiating out from the epicenter (i.e., central metropolis area with a high density of temple, palace, and residential structures). The Maya built extensive limestone terraces throughout the landscape for agricultural purposes, growing maize and other crops, such as beans and squash [48,55,56]. These terraces have been reported to cover as much as 80% of the study site and can be found along hill slopes and in valleys. The ability of airborne LiDAR to geolocate accurately ground surveyed terraces hidden below the forest canopy at Caracol has been previously documented [52,57,58]. From our visual analysis and digitization of the hillshaded digital elevation model (DEM), conspicuously terraced areas represented 28.7% of the landscape while conspicuously non-terraced areas represented 35.7%. Terraces at Caracol have been shown to increase soil depth along hill slope by up to 0.6 m [48]. Studies of other Maya terraces have shown that terraces effectively prevent soil erosion along slopes, increase soil depth, and increase moisture retention [59,60].

4. Methods

4.1. LiDAR Data Collection, Extraction, and Sampling

In April 2009, LiDAR was flown over the Caracol study site [61]. The LiDAR survey used an Optech GEMINI Airborne Laser Terrain Mapper (ALTM) mounted on a twin-engine Cessna Skymaster. During the 9.5 h of laser-on flight time, a total of 122 flight lines were flown ~800 m above the ground surface, 66 in a North-South direction and 60 in an East-West direction, allowing optimal penetration through the dense canopy. The swath width was 520 m and flight lines were placed 260 m apart, insuring a 200% overlap. The survey yielded ~20 points per m2, for a total of 4.28 billion measurements, of which 295 million were classified as ground returns (1.35 ground return points per m2 on average). The data were processed by NCALM (National Center for Airborne Laser Mapping); the final product included LiDAR point cloud files and a bare earth DEM with a 1 m horizontal resolution (see [62] for details explaining the DEM creation).
Three hundred non-overlapping 0.25 ha circular plots were randomly placed across the entire Caracol DEM using Hawth’s tools [63] to the east of a ~2.5 km-wide area near the Guatemala border which has been subjected to illegal clearing and selective logging [54]. Sample plots were stratified using terraced (n = 150) and non-terraced (n = 150) land use layers in ArcGIS that had been carefully digitized off the DEM (Figure 3). Other than terraces, these areas excluded areas with other Maya structures (e.g., causeways, residences, monuments). Samples were further stratified by low (0°–10°; n = 50), medium (10°–20°; n = 50), and high (20°–40°; n = 50) slopes to insure an even representation of slopes. Using FUSION LiDAR analysis software [64], LiDAR point returns were extracted for all elevation layers for the Caracol DEM and slope, aspect, and elevation values were obtained for each of the slope classes in the sample. ArcGIS 9.3 Spatial Analyst was used to generate slope, aspect, and plots. The Density Metrics function in FUSION permitted us to sample the 0.25 ha LiDAR point clouds by extracting slices associates with height ranges. Returns were sampled in 3 m height slices and sorted into bins from the forest floor to the canopy top (Figure 4) [65]. The final FUSION product yielded a data set of vertically binned height returns for each plot. All plots were sampled using a fixed grid with 10 × 10 m cells and all cells were summed for each height class. The proportion of total points was calculated for all height bins within each plot for the final height bin data set.
Remotesensing 06 10716 g003 1024
Figure 3. Renditions of 500 × 500 m (a) non-terraced and (b) terraced areas from Caracol. Locations of these example areas are shown in Figure 2.
Figure 3. Renditions of 500 × 500 m (a) non-terraced and (b) terraced areas from Caracol. Locations of these example areas are shown in Figure 2.
Remotesensing 06 10716 g003
Remotesensing 06 10716 g004 1024
Figure 4. Example of a (a) 0.25 ha point cloud of rainforest canopy and (b) associated histogram of proportions of returns binned into 3 m height intervals.
Figure 4. Example of a (a) 0.25 ha point cloud of rainforest canopy and (b) associated histogram of proportions of returns binned into 3 m height intervals.
Remotesensing 06 10716 g004

4.2. Statistical Analysis

The following forest structural characteristics were calculated using the LiDAR point height bins for each sample plot: average canopy height, canopy openness, and a vertical diversity index. Average canopy height was calculated by taking the average of the maximum heights for each 10 × 10 m window in the 0.25 ha plot. The measure for canopy openness was calculated by taking the ratio of ground returns divided by the sum of all other returns (ground returns/all non-ground returns) [66]. Jost diversity index was calculated for the proportion of returns that had been binned into 3-m height classes for each plot and used as an approximation of vertical diversity [67].
Permutational multivariate analysis of variance (PerMANOVA) was used to test for significant differences in forest structure between non-terraced and terraced areas. PerMANOVA allows for a non-parametric analysis of variance, which is ideal for non-normal data sets. PerMANOVA first calculates a distance matrix using a user selected distance measure. The test statistic (F-ratio) is calculated directly from the distance matrix. Permutations are then used to generate p-values to determine significance [68]. The Adonis function in the vegan package in R was used to perform the PerMANOVA analyses in our study [69].
For the first analysis, land use (terraced vs. non-terraced) was used as our factor, while slope, aspect, and elevation were held as covariates. A distance matrix (response variable) was calculated from the measured forest structural characteristics (i.e., average canopy height, canopy openness, and vertical diversity index) using the vegdist routine in the vegan package [69] for the R statistical software program. Vegdist uses the Bray-Curtis (Sorenson) distance measure, which is appropriate for community ecology datasets [70]. In a second PerMANOVA, we used a distance matrix calculated directly from the point return height bin data set for each plot. For the second analysis, land use was retained as our factor, while slope, aspect, and elevation were held as covariates. A total of 9999 permutations were used for each analysis.

5. Results and Discussion

PerMANOVA results from the LiDAR derived forest measurements indicated significant differences between terraced and non-terraced land uses (p = 0.016). Slope was also highly significant in explaining the variation between samples (p = 0.001), as was elevation (p = 0.001). The interaction of slope and aspect (p = 0.026), as well as the interaction of slope and elevation (p = 0.001), were also considered significant in explaining the variation seen between samples (Table 1).
Table
Table 1. Results from the PerMANOVA of the distance matrix constucted from LiDAR-derived forest measurements.
Table 1. Results from the PerMANOVA of the distance matrix constucted from LiDAR-derived forest measurements.
Sources of VariationdfMean SquareFr2p
Land Use Type *10.0245.040.0130.016
Slope10.13450.820.1340.001
Elevation10.02810.740.0280.001
Slope: Aspect10.0224.540.0120.026
Slope: Elevation10.04215.780.0410.001
Residuals2840.005 0.748
* Factor, all other sources of variation calculated as covariates.
Using the LiDAR height bin distance matrix, PerMANOVA revealed a significant proportion (p = 0.005) of the variation was explained by land use categories, i.e., terraced vs. not-terraced. Slope was again found to be significant in explaining the variation in the proportion of points found in each height bin (p = 0.001). In this analysis, aspect did not contribute significantly to the explanation of variation. In addition, elevation did not explain a significant proportion of the variation, but the interaction effects of slope: elevation and land use: slope were significant in explaining the variation of height bin distributions (Table 2).
Table
Table 2. Results from the PerMANOVA of the distance matrix constructed from the height bin LiDAR data.
Table 2. Results from the PerMANOVA of the distance matrix constructed from the height bin LiDAR data.
Sources of VariationdfMean SquareFr2p
Land Use Type *10.2275.020.0150.005
Slope11.04823.210.0680.001
Land Use Type: Slope10.1703.760.0110.018
Slope: Elevation10.48210.680.0310.001
Residuals2840.045 0.083
* Factor, all other sources of variation calculated as covariates.
When analyzed together as a distance matrix, the forest structure variables (canopy height, canopy openness, and vertical diversity index) were significantly impacted by the presence of terraces. When the individual trend lines of our three forest structural variables are graphed (Figure 5), a few patterns emerged. On non-terraced plots there is a trend of vertical diversity decreasing as the slope increases; however, this decrease occurs at a slower rate for terraced land use. A similar pattern exists with average canopy height, with canopy height decreasing as slope increases, but this decrease occurs at a slower rate on terraced land. Canopy openness increases as slope increase, with canopy openness on terraces increasing at a slower rate compared to non-terraced land. At high slope conditions average canopy height and vertical canopy diversity are the most pronounced.
Histograms generated from the LiDAR height bin data showed a similar trend, that is, non-terraced bins showed a distinct gradient among low, medium, and high slopes, with medium slope point values falling between the low and high slope. However, on terraced land there is a trend of reduced variation among slope categories. Within a height bin, different slope categories do not show as steep “stair step” pattern in terms of LiDAR point returns (Figure 6). This difference was most pronounced in the 0–3 m, 21–24 m, 24–27 m, and 27–30 m height bins. Another effect of terraces that can be deduced from this figure is an upward shift in the height of median energy (i.e., HOME) [66] from non-terraced to terraced plots which is indicative of higher AGB.
The shaping of forest structure by topographic variables may be explained by differences in tree species and the effect of topographic position on tree growth. Observations from previous studies have noted that terraces contain flora, such as palms, that are typically found in forest valleys [71]. A sister study [72] showed how tree species composition varies across terraced and non-terraced areas, with terraces acting as a type of environmental “bridge” between slope and non-sloped areas. Prior studies in Mesoamerican forests have shown topography to strongly influence the composition of tree species, with forests in valleys, along slopes, and along ridges forming distinct tree communities [73,74]. Tree species with specific topographic requirements have a direct impact on forest structure. Furthermore, though tree morphology is fairly plastic, different species with their specific architectural constraints, to a certain degree, may contribute uniquely to the general canopy structure [31].
Remotesensing 06 10716 g005 1024
Figure 5. Relationships of LiDAR-derived values of (a,b) average canopy height (c,d) canopy openness, and (e,f) vertical canopy diversity from terraced and not terraced plots as a function of topographic slope. Significant differences between terraced and non-terraced areas in average values (detected with a t-test) for low (0°–10°), medium (10°–20°), and high (>20°) slopes are designated with p-values.
Figure 5. Relationships of LiDAR-derived values of (a,b) average canopy height (c,d) canopy openness, and (e,f) vertical canopy diversity from terraced and not terraced plots as a function of topographic slope. Significant differences between terraced and non-terraced areas in average values (detected with a t-test) for low (0°–10°), medium (10°–20°), and high (>20°) slopes are designated with p-values.
Remotesensing 06 10716 g005
Variation in species composition alone may not explain the difference observed in forest structure. Edaphic factors related to topography, i.e., nutrient, water, and light availability, directly influence tree growth [28,29]. Differences in forest structure over terraces can be explained by altered edaphic conditions [55,56,59]. Steeper hillsides typically have thinner soils and reduced water availability; terraces increase soil depth and water availability, which impacts forest structure [44,75]. Terraces also retain nutrients that would otherwise be leached out of the soil [59,71]. This study identified a significant interaction between slope and elevation on canopy structure (Table 1 and Table 2). As position upslope increases, nitrogen decreases, which can have an effect on the growth of trees, resulting in reduced basal area and canopy cover [29]. Beyond the implications for human land use legacies, this study illustrates how relatively small changes in topography can result in long-lasting changes to rainforest structure [44].
Remotesensing 06 10716 g006 1024
Figure 6. Histograms for average proportion of LiDAR point returns binned by 3-m height classes for (a) non-terraced and (b) terraced plots divided into low (0°–10°), medium (10°–20°), and high (>20°) sloped areas.
Figure 6. Histograms for average proportion of LiDAR point returns binned by 3-m height classes for (a) non-terraced and (b) terraced plots divided into low (0°–10°), medium (10°–20°), and high (>20°) sloped areas.
Remotesensing 06 10716 g006
These results indicate that the terraces constructed by the Maya over 1000 years ago continue to influence the forest structure at Caracol. The terraces have significantly altered the micro-topography of the terrain, resulting in a corresponding echo in forest structure. While topography (i.e., slope, elevation, and aspect) appears to act as the driving factor in forest structure variation, the addition of terraces dilutes the topographic gradient, reducing the variation in forest structure from low-lying valleys to highly-sloped hills.

6. Conclusions

Agricultural terracing transforms hillslope topography; these oft-hidden archaeological features can last for millennia and are readily delineated with LiDAR [52,57,58]. While likely engineered for soil and water conservation, here we used airborne LiDAR to reveal the persistent, unintentional impacts that ancient Maya terracing has had on the structure of the tropical rainforest that regenerated after agricultural abandonment [60]. This study showed that the presence of terraces dampened the effect of topographic slope on structural variables (i.e., height, openness, and vertical diversity) that define a forest canopy. With steeper slopes, terraces yielded ~8% taller, ~20% more closed-canopy, and ~7% more vertically-diverse forests. As canopy height decreased with an increase in slope on non-terraced lands, height differences were muted with terracing (Figure 5a,b). Canopy openness, a measure related to understory light penetration, increased as slope increased; however, on terraced areas, openness varied less as a function of slope (Figure 5c,d). Vertical canopy diversity, an estimate of vertical spatial heterogeneity that has been correlated to avian diversity [33], followed a similar pattern as average canopy height; its variance decreased along the topographic gradient with terracing (Figure 5e,f). Terraces mediated the effect of slope on the proportion of LiDAR returns in the vertical, 3-m height bins, i.e., when terraces were present, forest structure did not differ as much with slope as when terraces were absent (Figure 6). Though not directly measured, these canopy changes most likely correspond to higher levels of aboveground biomass or stored carbon [66].
The ability of LiDAR to concurrently measure ground and canopy surfaces elucidated this heretofore unknown, long-term anthropogenic impact on these forests. After Caracol was abandoned ca. AD 900, ancient Maya agricultural practices continue to influence contemporary forest structure. These effects should have consequences on ecosystem services (Figure 1) related to arboreal habitat and carbon sequestration abilities of this biodiverse, high-biomass region, which is part of the Mesoamerican Biological Corridor [76]. Though this study only examined terraces at Caracol, these anthropogenic features are found throughout the Maya region [59,77], including the Petén in Guatemala [78] and the southern Yucatán in Mexico [20]. With the prevalence of relic terraces throughout Central American landscapes and elsewhere [79,80,81] comes the understanding that past agriculture practices have left lasting legacies on ecosystem structure and function. LiDAR demonstrated the synergy between landscape archaeology and landscape ecology. This approach can be extended across other large expanses of temperate and tropical forests where humans have left significant fingerprints from past land use [3,8,12,81] that currently lay obscured below vegetation. Moreover, this remote sensing study gives us pause over our current land use practices [82] and reminds us that today is tomorrow’s past.

Acknowledgments

This research was supported by NASA Grant #NNX08AM11G awarded to John Weishampel through the Space Archaeology program and the University of Central Florida—University of Florida—Space Research Initiative (UCF-UF-SRI). The authors would like to thank the ongoing efforts of the Caracol Archaeological Project (PIs-Arlen and Diane Chase) which is administered through the Belize Institute of Archaeology. We would also like to thank James Angelo and Kim Medley for statistical assistance and Arlen Chase, Ross Hinkle, Pedro Quintana-Ascencio, Prasad Thenkabail and anonymous reviewers for providing improvements to the manuscript.

Author Contributions

The research idea was conceived by and funded through a grant obtained by John Weishampel who advised Jessica Hightower during her Master’s thesis on this topic. Jessica Hightower performed the research and analyzed the data along with John Weishampel. All authors interpreted the results and contributed to the writing of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chazdon, R.L. Tropical forest recovery: Legacies of human impact and natural disturbances. Perspect. Plant Ecol. 2003, 6, 51–71. [Google Scholar]
  2. Foster, D.R.; Swanson, F.; Aber, J.; Burke, I.; Brokaw, N.; Tilman, D.; Knapp, A. The importance of land-use legacies to ecology and conservation. BioScience 2003, 53, 77–88. [Google Scholar]
  3. Ruddiman, W.F. Earth Transformed; W.H. Freeman and Co.: New York, NY, USA, 2014. [Google Scholar]
  4. Flinn, K.M.; Vellend, M. Recovery of forest plant communities in post-agricultural landscapes. Front Ecol. Environ. 2005, 3, 243–250. [Google Scholar]
  5. Sponsel, L.E.; Bailey, R.C.; Headland, T.N. Anthropological perspectives on the causes, consequences, and solutions of deforestation. In Tropical Deforestation: The Human Dimension; Sponsel, L.E., Headland, T.N., Bailey, R.C., Eds.; Columbia Univesity Press: New York, NY, USA, 1996; pp. 3–52. [Google Scholar]
  6. Laurance, W.F. Reflections of the tropical deforestation crisis. Biol. Conserv. 1999, 91, 109–117. [Google Scholar]
  7. Achard, F.; Eva, H.D.; Stibig, H.; Mayaux, P.; Gallego, J.; Richards, T.; Malingreau, J. Determination of deforestation rates of the world’s humid tropical forests. Science 2002, 297, 999–1002. [Google Scholar]
  8. Bellemare, J.; Motzkin, G.; Foster, D.R. Legacies of the agricultural past in the forested present: An assessment of historical land-use effects on rich mesic forests. J. Biogeogr. 2002, 29, 1401–1420. [Google Scholar]
  9. Sanford, R.L., Jr.; Horn, S.P. Holocene rain-forest wilderness: A neotropical perspective on humans as an exotic, invasive species. In USDA Forest Service RMRS-P-15-VOL-3, Proceedings of Wilderness Science in a Time of Change; McCool, S.F., Cole, D.N., Eds.; Rocky Mountain Research Station: Ogden, UT, USA, 2000; pp. 168–173. [Google Scholar]
  10. Gomez-Pompa, A.; Allen, M.F.; Fedick, S.L.; Jimenez-Osornio, J.J. The Maya Lowlands: A case study for the future? Conclusions. In The Lowland Maya Area: Three Millennia at the Human-Wildland Interface; Gomez-Pompa, A., Allen, M.F., Fedick, S.L., Jimenez-Osornio, J.J., Eds.; Hawthorn Press, Inc.: New York, NY, USA, 2003; pp. 623–631. [Google Scholar]
  11. Clark, D.B. Abolishing virginity. J. Trop. Ecol. 1996, 12, 735–739. [Google Scholar]
  12. Heckenberger, M.J.; Kuikuro, A.; Kuikuro, U.T.; Russell, J.C.; Schmidt, M.; Fausto, C.; Franchetto, B. Amazonia 1492: Pristine forest or cultural parkland. Science 2003, 301, 1710–1714. [Google Scholar]
  13. Delcourt, H.R. The impact of prehistoric agriculture and land occupation on natural vegetation. Trends Ecol. Evol. 1987, 2, 39–44. [Google Scholar]
  14. Butzer, K.W. Ecology in the long view: Settlement histories, agrosystemic strategies, and ecological performance. J. Field Archaeol. 1996, 23, 141–150. [Google Scholar]
  15. Mann, C.C. 1491: New Revelations of the Americas before Columbus; Knopf Press: New York, NY, USA, 2005. [Google Scholar]
  16. Peterken, G.F.; Game, M. Historical factors affecting the number and distribution of vascular plant species in the woodlands of central Lincolnshire. J. Ecol. 1984, 72, 155–182. [Google Scholar]
  17. Motzkin, G.; Foster, D.; Allen, A.; Harrod, J.; Boone, R. Controlling site to evaluate history: Vegetation patterns of a New England sand plain. Ecol. Monogr. 1996, 66, 345–365. [Google Scholar]
  18. Briggs, J.M.; Spielmann, K.A.; Schaafsma, H.; Kintigh, K.W.; Kruse, M.; Morehouse, K.; Schollmeyer, K. Why ecology needs archaeologists and archaeology needs ecologists. Front. Ecol. Environ. 2006, 4, 180–188. [Google Scholar]
  19. Dambrine, E.; Dupouey, J.L.; Laut, L.; Humbert, L.; Thinon, M.; Beaufils, T.; Richard, H. Present forest biodiversity patterns in France related to former Roman agriculture. Ecology 2007, 88, 1430–1439. [Google Scholar]
  20. Turner, B.L., II; Klepeis, P.; Schneider, L.C. Three millennia in southern Yucatan peninsula: Implications for occupancy, use, and carrying capacity. In The Lowland Maya Area: Three Millennia at the Human-Wildland Interface; Gomez-Pompa, A., Allen, M.F., Fedick, S.L., Jimenez-Osornio, J.J., Eds.; Hawthorn Press, Inc.: New York, NY, USA, 2003; pp. 361–373. [Google Scholar]
  21. Gómez-Pompa, A.; Flores, J.S.; Sosa, V. The “pet kot”: A man-made tropical forest of the Maya. Interciencia 1987, 12, 10–15. [Google Scholar]
  22. Lambert, J.D.H.; Arnason, T. Distribution of vegetation on Maya ruins and its relationship to ancient land-use at Lamanai, Belize. Turrialba 1978, 28, 33–41. [Google Scholar]
  23. Lambert, J.D.H.; Arnason, J.T. Rámon and Maya ruins: An ecological, not an economic, relation. Science 1982, 216, 298–299. [Google Scholar]
  24. Harper, K.A.; MacDonald, S.E.; Burton, P.J.; Chen, J.; Brosofske, K.D.; Saunders, S.C.; Euskirchen, E.S.; Roberts, D.; Jaiteh, M.S.; Esseen, P. Edge influence on forest structure and composition in fragmented landscapes. Conserv. Biol. 2005, 19, 768–782. [Google Scholar]
  25. Turner, M.G. Disturbance and landscape dynamics in a changing world. Ecology 2010, 91, 2833–2849. [Google Scholar]
  26. Aide, M.T.; Zimmerman, J.K.; Pascarella, J.B.; River, L.; Marcano-Vega, H. Forest regeneration in a chronosequence of tropical abandoned pastures: Implication for restoration ecology. Restor. Ecol. 2000, 8, 328–338. [Google Scholar]
  27. Mueller, A.D.; Islebe, G.A.; Anselmetti, F.S.; Ariztegui, D.; Brenner, M.; Hodell, D.A.; Hajdas, I.; Hamann, Y.; Haug, G.H.; Kennett, D.J. Recovery of the forest ecosystem in the tropical lowlands of northern Guatemala after disintegration of Classic Maya polities. Geology 2010, 38, 523–526. [Google Scholar]
  28. Clark, D.B.; Clark, D.B. Landscape-scale variation in forest structure and biomass in a tropical rain forest. For. Ecol. Manag. 2000, 137, 185–198. [Google Scholar]
  29. Tateno, R.; Takeda, H. Forest structure and tree species distribution in relation to topography-mediated heterogeneity of soil nitrogen and light at the forest floor. Ecol. Res. 2003, 18, 559–571. [Google Scholar]
  30. Spies, T.A. Forest structure: A key to the ecosystem. Northwest Sci. 1998, 72, 34–36. [Google Scholar]
  31. Alves, L.F.; Vieira, S.A.; Scaranello, M.A.; Camargo, P.B.; Santos, F.A.M.; Joly, C.A.; Martinelli, L.A. Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil). For. Ecol. Manag. 2010, 260, 679–691. [Google Scholar]
  32. Turner, M.G. Landscape ecology: The effect of pattern on process. Annu. Rev. Ecol. Syst. 1989, 20, 171–197. [Google Scholar]
  33. MacArthur, R.H.; MacArthur, J.W. On bird species diversity. Ecology 1961, 42, 594–598. [Google Scholar]
  34. Verschuyl, J.P.; Hansen, A.J.; McWethy, D.B.; Sallabanks, R.; Hutto, R.L. Is the effect of forest structure on bird diversity modified by forest productivity. Ecol. Appl. 2008, 18, 1155–1170. [Google Scholar]
  35. Müller, J.; Bae, S.; Röder, J.; Chao, A.; Didham, R.K. Airborne LiDAR reveals context dependence in the effects of canopy architecture on arthropod diversity. For. Ecol. Manag. 2014, 312, 129–137. [Google Scholar]
  36. Alvarez, E.; Duque, A.; Saldarriaga, J.; Cabrera, K.; de las Salas, G.; del Valle, I.; Lema, A.; Moreno, F.; Orrego, S.; Rodríguez, L. Tree above-ground biomass allometries for carbon stocks estimation in the natural forests of Columbia. For. Ecol. Manag. 2012, 267, 297–308. [Google Scholar]
  37. Prentice, I.C.; Leemans, R. Pattern and process and the dynamics of forest structure: A simulation approach. J. Ecol. 1990, 78, 340–355. [Google Scholar]
  38. Brokaw, N.V.L. Gap-phase regeneration in a tropical forest. Ecology 1992, 66, 682–687. [Google Scholar]
  39. Pacala, S.W.; Deutschman, D.H. Details that matter: The spatial distribution of individual trees maintains forest ecosystem function. Oikos 1995, 74, 357–365. [Google Scholar]
  40. Phillips, O.L.; Baker, T.R.; Arroyo, L.; Higuchi, N.; Killeen, T.; Laurance, W.F.; Lewis, S.L.; Lloyd, J.; Malhi, Y.; Monteagudo, A.; et al. Pattern and process in Amazon forest dynamics, 1976–2001. Philos. Trans. R Soc. B 2004, 359, 381–407. [Google Scholar]
  41. Shugart, H.H.; Saatchi, S.; Hall, F.G. Importance of structure and its measurement in quatifying function of forest ecosystems. J. Geophys. Res. 2010, 115, G00E13. [Google Scholar] [CrossRef]
  42. Urquiza-Haas, T.; Dolman, P.M.; Peres, C.A. Regional scale variation in forest structure and biomass in the Yucatán Peninsula: Effects of forest disturbance. For. Ecol. Manag. 2007, 247, 80–90. [Google Scholar]
  43. Holdridge, L.R.; Grenke, W.C.; Hatheway, W.H.; Liang, T.; Tosi, J.A. Forest Environments in Tropical Life Zones: A Pilot Study; Pergamon: New York, NY, USA, 1971. [Google Scholar]
  44. Detto, M.; Muller-Landau, H.C.; Mascaro, J.; Asner, G.P. Hydrological networks and associated topographic variation as templates for the spatial organization of tropical forest vegetation. PLoS One 2013, 8, e76296. [Google Scholar] [CrossRef]
  45. Homeier, J.; Breckle, S.W.; Günter, S.; Rollenbeck, R.T.; Leuschner, C. Tree diversity, forest structure and productivity along altitudinal and topographical gradients in a species-rich Ecuadorian montane rain forest. Biotropica 2010, 42, 140–148. [Google Scholar]
  46. Weishampel, J.F.; Drake, J.B.; Cooper, A.; Blair, J.B.; Hofton, M. Forest canopy recovery from the 1938 hurricane and subsequent damage measured with airborne LiDAR. Remote Sens. Environ. 2007, 190, 142–153. [Google Scholar]
  47. Laurance, W.F.; Ferreira, L.V.; Rankin-de Merona, J.M.; Laurance, S.G. Rain forest fragmentation and the dynamics of Amazonian tree communities. Ecology 1998, 79, 2032–2040. [Google Scholar]
  48. Coultas, C.L.; Collins, M.C.; Chase, A.F. Some soils common to Caracol, Belize and their significance to ancient agriculture and land-use. In Studies in the Archaeology of Caracol, Belize; Chase, D., Chase, A., Eds.; Pre-Columbian Art Research Institute: San Francisco, CA, USA, 1994; Monograph 7; pp. 21–33. [Google Scholar]
  49. Chase, A.F.; Chase, D.Z. Scale and intensity in classic period Maya agriculture: Terracing and settlement at the “Garden City” of Caracol, Belize. Cult. Agric. 1998, 20, 60–77. [Google Scholar]
  50. Opitz, R.S.; Cowley, D.C. Interpreting Archaeological Topography: 3D Data; Visualisation and Observation Oxbow Books: Oxford, UK, 2013. [Google Scholar]
  51. Chase, A.F.; Chase, D.Z.; Weishampel, J.F. Lasers in the jungle: Airborne sensors reveal a vast Maya landscape. Archaeology 2010, 63, 27–29. [Google Scholar]
  52. Weishampel, J.F.; Hightower, J.N.; Chase, A.F.; Chase, D.Z. Remote sensing of below canopy land use features from the Maya polity of Caracol. In Understanding Landscapes, from Discovery to Their Spatial Organization; British Archaeological Reports S2541; Djinjian, F., Robert, S., Eds.; Archaeopress: Oxford, UK, 2013; pp. 131–136. [Google Scholar]
  53. Friesner, J. Hurricanes and the Forests of Belize; Forest Department, Ministry of Natural Resources and the Environment: Belmopan City, Belize, 1993; p. 20. [Google Scholar]
  54. Weishampel, J.F.; Hightower, J.N.; Chase, A.F.; Chase, D.Z. Use of airborne LiDAR to delineate canopy degradation and encroachment along the Guatemala—Belize border. Trop. Conserv. Biol. 2012, 5, 12–24. [Google Scholar]
  55. Webb, E.A.; Schwarcz, H.P.; Healy, P.F. Detection of ancient maize in lowland Maya soils using stable carbon isotopes: Evidence from Caracol, Belize. J. Archaeol. Sci. 2004, 31, 1039–1052. [Google Scholar]
  56. Murtha, T. Land and Labor: Maya Terraced Agriculture: An Investigation of the Settlement Economy and Intensive Agricultural Landscape of Caracol, Belize; Verlag, D.M., Ed.; Muller: Saarbruckan, Germany, 2009. [Google Scholar]
  57. Chase, A.F.; Chase, D.Z.; Weishampel, J.F.; Drake, J.B.; Shrestha, R.L.; Slatton, K.C.; Awe, J.J.; Carter, W.E. Airborne LiDAR, archaeology, and the ancient Maya landscape at Caracol, Belize. J. Archaeol. Sci. 2011, 38, 387–398. [Google Scholar]
  58. Chase, A.F.; Chase, D.Z.; Fisher, C.T.; Leisz, S.J.; Weishampel, J.F. Geospatial revolution and remote sensing LiDAR in Mesoamerican archaeology. PNAS 2012, 109, 12837–12838. [Google Scholar]
  59. Beach, T.; Luzzadder-Beach, S.; Dunning, N.; Hageman, J.; Lohse, J. Upland agriculture in the Maya lowlands: Ancient Maya soil conservation in northwestern Belize. Geogr. Rev. 2002, 92, 372–397. [Google Scholar]
  60. Chase, A.S.Z.; Weishampel, J.F. Water capture and agricultural terracing at Caracol, Belize as revealed through the 2009 LiDAR campaign. 2014; in revision. [Google Scholar]
  61. Weishampel, J.F.; Chase, A.F.; Chase, D.Z.; Drake, J.B.; Shrestha, R.L.; Slatton, K.C.; Awe, J.J.; Hightower, J.; Angelo, J. Remote sensing of ancient Maya land use features at Caracol, Belize related to tropical rainforest structure. In Proceedings of the SpaceTimePlace: Third International Conference on Remote Sensing in Archaeology, Tamil Nadu, India, 17–21 August 2009; British Archaeological Reports S2118. Campana, S., Forte, M., Liuzza, C., Eds.; Archaeopress: Oxford, UK, 2010; pp. 45–52. [Google Scholar]
  62. Fernandez-Diaz, J.C.; Carter, W.E.; Shrestha, R.L.; Glennie, C.L. Now you see it…now you don’t: Understanding airborne mapping LiDAR collection and data product generation for archaeological research in Mesoamerica. Remote Sens. 2014, 6, 9951–10001. [Google Scholar]
  63. Beyer, H.L. Hawth’s Analysis Tools for ArcGIS. Available online: http://www.spatialecology.com/htools (accessed on 21 March 2012).
  64. McGaughey, R.J. FUSION/LDV: Software for LIDAR Data Analysis and Visualization; US Department of Agriculture, Forest Service, Pacific Northwest Research Station: Seattle, WA, USA, 2009; p. 123. [Google Scholar]
  65. Angelo, J.J.; Duncan, B.W.; Weishampel, J.F. Using LiDAR-derived vegetation profiles to predict time since fire in an oak scrub landscape in east-central Florida. Remote Sens. 2010, 2, 514–525. [Google Scholar]
  66. Drake, J.B.; Dubayah, R.O.; Clark, D.B.; Knox, R.G.; Blair, J.B.; Hofton, M.A.; Chazdon, R.L.; Weishampel, J.F.; Prince, S. Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sens. Environ. 2002, 79, 305–319. [Google Scholar]
  67. Jost, L. Entropy and diversity. Oikos 2006, 113, 363–365. [Google Scholar]
  68. Anderson, M.J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 2001, 26, 32–46. [Google Scholar]
  69. Oksanen, J.; Blanchet, F.G.; Kindt, R.; Legendre, P.; O’Hara, R.B.; Simpson, G.L.; Solymos, P.; Stevens, M.H.H.; Wagner, H. Vegan: Community Ecology Package. R Package Version 1.17–9. Available online: http://CRAN.R-project.org/package=vegan (accessed on 21 March 2012).
  70. McCune, B.; Grace, J.B. Analysis of Ecological Communities; MjM Software Design: Gleneden Beach, OR, USA, 2002. [Google Scholar]
  71. Healy, P.F.; Lambert, J.D.H.; Arnason, J.T.; Hebda, R.J. Caracol, Belize: Evidence of ancient Maya agricultural terraces. J. Field Archaeol. 1983, 10, 397–410. [Google Scholar]
  72. Hightower, J.N. Relating Ancient Maya Land Use Legacies to the Contemporary Forest of Caracol, Belize. Master’s Thesis, University of Central Florida, Orlando, FL, USA, 2012; p. 71. [Google Scholar]
  73. Brewer, S.T.; Rejmanek, M.; Webb, A.H.; Fine, P.V.A. Relationship of phytogeagraphy and diversity of tropical tree species with limestone topography in southern Belize. J. Biogeogr. 2003, 30, 1669–1688. [Google Scholar]
  74. White, D.A.; Hood, C.S. Vegetation patterns and environmental gradients in tropical dry forests of the northern Yucatan Peninsula. J. Veg. Sci. 2004, 15, 151–160. [Google Scholar]
  75. Furley, P.A.; Newey, W.W. Variations in plant communities with topography over tropical limestone soils. J. Biogeogr. 1979, 6, 1–15. [Google Scholar]
  76. DeClerck, F.A.J.; Chazdon, R.; Holl, K.D.; Milder, J.C.; Finegan, B.; Martinez-Salinas, A.; Imbach, P.; Canet, L.; Ramos, Z. Biodiversity conservation in human-modified landscapes of Mesoamerica: Past, present, and future. Biol. Conserv. 2010, 143, 2301–2313. [Google Scholar]
  77. Wyatt, A.R. Gardens on Hills: Ancient Maya Terracing and Agricultural Production at Chan, Belize. PhD Dissertation, University of Illinois, Chicago, IL, USA, 2008; p. 449. [Google Scholar]
  78. Beach, T.; Dunning, N.P. Ancient Maya terracing and modern conservation in the Peten rain forest of Guatemala. J. Soil Water Conserv. 1995, 50, 138–145. [Google Scholar]
  79. Ackermann, O.; Svoray, T.; Haiman, M. Nari (calcrete) outcrop contributions to ancient agricultural terraces in the Southern Shephelah, Israel: Insights from digital terrain analysis and a geoarchaeological field survey. J. Archaeol. Sci. 2008, 35, 930–941. [Google Scholar]
  80. Pretto, F.; Celesti-Grapow, L.; Carli, E.; Blasi, C. Influence of past land use and current human disturbance on non-native plants species on small Italian islands. Plant Ecol. 2010, 2010, 225–239. [Google Scholar]
  81. McCoy, M.D.; Asner, G.P.; Graves, M.W. Airborne lidar survey of irrigated agricultural landscapes: An application of the slope contrast method. J. Archaeol. Sci. 2011, 38, 2141–2154. [Google Scholar]
  82. Nassauer, J.I.; Raskin, J. Urban vacancy and land use legacies: A frontier for urban ecological research, design, and planning. Landsc. Urban Plan. 2014, 125, 245–253. [Google Scholar]

Share and Cite

MDPI and ACS Style

Hightower, J.N.; Butterfield, A.C.; Weishampel, J.F. Quantifying Ancient Maya Land Use Legacy Effects on Contemporary Rainforest Canopy Structure. Remote Sens. 2014, 6, 10716-10732. https://doi.org/10.3390/rs61110716

AMA Style

Hightower JN, Butterfield AC, Weishampel JF. Quantifying Ancient Maya Land Use Legacy Effects on Contemporary Rainforest Canopy Structure. Remote Sensing. 2014; 6(11):10716-10732. https://doi.org/10.3390/rs61110716

Chicago/Turabian Style

Hightower, Jessica N., A. Christine Butterfield, and John F. Weishampel. 2014. "Quantifying Ancient Maya Land Use Legacy Effects on Contemporary Rainforest Canopy Structure" Remote Sensing 6, no. 11: 10716-10732. https://doi.org/10.3390/rs61110716

Article Metrics

Back to TopTop