Anthropogenic land degradation affects many biogeophysical processes,
including reductions of net primary production (NPP). Degradation occurs at
scales from small fields to continental and global. While measurement and
monitoring of NPP in small areas is routine in some studies, for scales
larger than 1 km
Land degradation is a deleterious process in which unfavorable conditions for humans occur (Pickup, 1998, 1996; Safriel, 2007; Safriel and Adeel, 2005) as a result of direct and indirect human and natural processes. In drylands (aridity index < 0.65), poor land management such as excessive cultivation, overgrazing, and unmanaged fires have far-reaching effects on biogeophysical processes (Prince, 2002). While degradation is always undesirable, there is evidence that, in some cases, it cannot be reversed (Prince, 2016) when the causes are removed – a much more serious outcome. However, it is not known how widespread this condition is. There are many other aspects of dryland degradation that are little understood, including its location, severity, and actions needed for remediation (Reynolds et al., 2007) or, at least, to prevent a net increase (Lal et al., 2012; UNCCD, 2012). The extent of soil or pasture degradation through overgrazing, anywhere in the world, has been estimated by experts' subjective opinion, rather than systematic quantitative criteria (Gifford, 2010).
“Degradation” implies an undesirable condition compared with a starting point (Prince, 2016), but degraded compared to what? To detect a relative condition, a reference is needed, in this case not degraded (Bastin et al., 2012; Boer and Smith, 2003; Prince et al., 2007; Stoms and Hargrove, 2000) without which states of degradation have no meaning. However, the detection of non-degraded reference sites that are at their potential is problematic (Wessels et al., 2007). There are several approaches that seem reasonable but have severe limitations, particularly when applied to large areas: visual assessment of satellite imagery is entirely subjective and therefore unrepeatable; field surveys, such as the National Resources Inventory (Nusser and Goebel, 1997), are limited to small areas (Budde et al., 2004; O'Connor et al., 2001; Prince, 2004) that can be assessed by an evaluator on the ground; and process modeling of potential production followed by comparison with actual production (Bai et al., 2008; Boer and Puigdefabregas, 2005) suffers from the need for data and parameters that are generally not available (Prince, 2002).
Location of the Burdekin Dry Tropics (BDT) region in the state of Queensland, Australia, the six major river basins, and major roads and towns.
The particular type of degradation investigated here is anthropogenic
reduction of net primary production (NPP), which, in addition to its own
importance, is an indicator of a wider range of degradative processes
(Prince, 2002) such as soil compaction, salinization, and water and wind erosion
that generally also reduce NPP (Pickup, 1996; Walker and Janssen, 2002). The
objective of this study was to identify and characterize the extent and
severity of degradation of vegetation productivity in the extensive
rangelands, in excess of 10 000 km
Specifically, this study (1) identified the spatial extent of non-degraded and degraded land, (2) distinguished significant land trends in interannual reductions in NPP, and (3) linked total NPP reductions to specific land processes and states in the BDT.
The BDT region is located in northern Queensland, Australia, and covers
approximately 7.45
Annual average rainfall in BDT for 2000–2013. The dashed line is the 14-year average.
In the BDT, NPP is strongly influenced by regional variations in moisture availability (Hutley et al., 2000), fire frequency (Beringer et al., 2007), and soil properties. Native vegetation varies from dense to sparse forest to shrubland and open grassland. Approximately 83 % of the BDT is savanna consisting of mixed grass and trees. There are smaller areas that consist exclusively of shrubs (1 %), grasses (8 %), or rain-fed crops (8 %). The ratio of tree cover to grass cover is a defining attribute that differentiates local environments in savanna ecosystems (Accatino et al., 2010). The croplands, both irrigated and rain-fed, are found in northeastern, higher rainfall areas.
The major land use (85–90 % of the BDT) is livestock production on unimproved pastures (Mellick and Hanlon, 2005). According to the State of Queensland (2011), approximately 12 % of the BDT has grazing practices likely to result in degradation.
Land capability classes (LCCs) are areas that are homogeneous with respect
to the selected environmental factors. The factors used here were
meteorological, soil, and vegetation. The Australian Bureau of Meteorology
distributes daily, synoptic weather reports consisting of rainfall (Weymouth
et al., 1999), minimum and maximum temperature, water vapor pressure deficit
at 09:00 and 15:00, and solar exposure (Jones et al., 2009), gridded at 5
A
Few maps exist that could be used for validation of the homogeneity of LCCs
in the BDT. One such is the grazing land management (GLM) land types map
(DPI&F, 2004; Whish, 2011), which classifies areas based on vegetation,
soil, and terrain characteristics to create static types within which the
response to grazing pressure is similar. Since the principles used to create
GLM were similar to those of the UMDLCCs, an additional LNS was performed
using GLM land types (GLMLCC). The vector GLM map was converted to a raster
format at a 250
The two LCCs were compared using the mean square variance of their maximum
NPP to determine the extent to which each reduced within-LCC variance and
maximized between-LCC variance. Interannual wet season rainfall (November to
April) was averaged throughout the BDT (Fig. 2), and then compared with the two
variance components of both UMDLCC and GLMLCC. A paired
A second comparison was made using the Vegetation Assets, States and Transitions (VAST) classification of Australia, version 2 (Lesslie et al., 2010). VAST is a national level map of changes to vegetation since European settlement, which began in 1750, showing the degree of anthropogenic modification of native vegetation until 2011. VAST uses the following classes: wilderness, biophysical naturalness, land use, land cover, and extent of native vegetation. There are four classes of increasing human modification: 1, “modified”; 2, “transformed”; 3, “replaced”; and 4, “removed”. Areas without naturally occurring native vegetation are designated 5 (“bare”) and areas with no change 0 (“residual”).
Erosion is strongly linked to land degradation in drylands (Lal, 2003; Ravi
et al., 2010), and this is the case in Australian rangelands (Bui et al.,
2011; Dregne, 1995; Gillieson et al., 1996; Webb et al., 2009). A database of
erosion was used to better understand the nature of the degradation that was
detected. Four environmental variables related to natural and human-related
erosion processes were used: sediment load at 500
Moderate Resolution Imaging Spectrometer (MODIS) NPP data (MOD17A3) (Running
et al., 2004) were obtained from the Land Processes Distributed Active
Archive Center satellite data archives (
LNS is spatially and temporally scale-dependent since the NPP in a pixel is the sum of its finer-scale components and is calculated for individual years over a 14-year period. Therefore, in this
application, degradation at finer spatial and temporal scales than
250
LNS values are the difference between each pixel and its reference NPP
(Fig. 3). It is therefore zero (equal to the reference NPP, i.e., not degraded)
or negative (below the reference, i.e., degraded). The LNS values can be
expressed as the actual reduction of NPP in gC m
Example of the use of the frequency distribution of NPP of pixels in a single land capability class (LCC) to calculate local NPP scaling (LNS) values. The vertical line denotes the reference NPP at the 85 percentile of the distribution. The abscissa is labeled in LNS, NPP, and percentage LNS units.
The potential, non-degraded reference NPP was obtained using the frequency distribution of NPP in each LCC (Fig. 3). The 85th percentile was arbitrarily selected as the best estimator. Pixels with NPP higher than the reference, possibly caused by residual pixels with high NPP in areas that were not typical of the rest of the LCC, were omitted. A possible limitation of LNS is if no pixels are at their maximum; the reference would then be below the true potential. This effect was minimized by masking rivers, open water, roads, human settlements, and other human land features not representative of the LCC, but it cannot be entirely eliminated and so interpretation of the results must take this into account.
LNS percent values were averaged from 2000 to 2013 to determine the mean NPP
reduction for each pixel over the 14 years. To facilitate discussion, values
that were
Spatial agreement between average LNS values and ecological indicators
related to land condition (e.g., hillslope and gully erosion) or
susceptibility to poor condition (e.g., rainfall erosivity and soil
erodibility) were examined using Cohen's kappa (
The average number of pixels per UMDLCC varied each year from 3182
(0.01
The interannual, between-LCC variance in reference NPP was higher for
UMDLCC compared with GLMLCC. Conversely, within-LCC variance for UMDLCC was
lower than for GLMLCC, indicating that the pixels selected as reference
within UMDLCCs were more homogeneous than GLMLCC and more distinct between.
A paired
Interannual rainfall was significantly related to between-LCC and within-LCC variance in reference NPP for both LCCs (Fig. 4), accounting for nearly equal proportions of within-LCC variance in reference NPP for UMDLCC and GLMLCC (Fig. 4b), but between-LCC variance was better accounted for by UMDLCC (81 %) than for GLMLCC (66 %; Fig. 4a).
Mean square variance in reference NPP (MgC m
Mean, standard deviation, and
The comparison of UMDLCC and the VAST land classification, albeit based on
different data and aims, provided an independent comparison. In total, 35.8 % of
UMDLCC reference pixels were in the VAST “residual” class that has,
theoretically, been undisturbed since 1750. The remaining 64.2 % were in
classes with varying degrees of vegetation changes from native pasture:
1, “modified” (29.6 %); 2, “transformed” (19.2 %); and 3, “replaced”
(15.3 %). The remaining reference sites, less than 1 %, were in classes
4 (“removed”) or 5 (“bare”) with LCCs where all pixels were
degraded or have been caused by inadequate or inaccurate data used to
create the LCCs, errors in the VAST classification, or a result of
re-gridding VAST pixels from 1
The
Average LNS (Mg C m
The sum of LNS values for an entire class, as opposed to the LNS value per
unit area, revealed the importance of class size in contributing to the
overall reduction in NPP. The “degraded” class had a total reduction in NPP
of
Area and percentage of Burdekin Dry Tropics in each LNS range.
Local net production scaling (LNS) in the Burdekin Dry Tropics (BDT)
The majority of degraded pixels had LNS values between
The extent of “degraded” and “non-degraded” areas varied between the six major river basins (Tables 4 and 5). Two of these, Belyando and Suttor, comprised 67 % of all “degraded” areas in the entire BDT, while the Bowen Broken Bogie had the lowest (2 %) (Table 4). Despite being the first and third largest basins in the BDT (“degraded” plus “non-degraded” pixels) the Upper Burdekin and Cape Campaspe had only the third and fourth most “degraded” pixels (Table 4), respectively. However, “non-degraded” area decreased with decreasing size of each river basin (Table 5).
Degraded LNS class. Area, severity, and variation in LNS and LNS percent. SD – standard deviation.
Non-degraded LNS class. Area, severity, and variation in LNS and LNS percent. SD – standard deviation.
The severity of reductions in NPP, indicated by the average LNS, varied surprisingly little between river basins (Tables 4 and 5). The most severely degraded were in the Lower Burdekin, Bowen Broken Bogie, and Upper Burdekin (Table 4). The Upper Burdekin also had the most severe reductions of non-degraded pixels (Table 5). The Belyando and Cape Campaspe had the least severe reductions in NPP of degraded and non-degraded pixels, respectively. The average LNS and its percentage of the reference NPP for degraded and non-degraded pixels, however, were all within 1 standard deviation, suggesting that the reductions in NPP for each river basin did not differ substantially.
Among degraded areas there was evidence of managed grazing, including abrupt differences in LNS along station boundaries (Fig. 5b), but there were also gradients of LNS within a single station (Fig. 5c), and others with low LNS spread across multiple boundaries (Fig. 5d). Other areas with evidence of management included forest clearing (Fig. 5e) near station boundaries. There were also locations classified as degraded, with little evidence of direct grazing management such as between the drainage lines of streams (Fig. 5f).
Across the entire BDT there was substantial interannual variation in LNS, particularly in areas with low values (Fig. 6a). In years with high rainfall (e.g., 2000, 2008, 2009, and 2011) compared with low rainfall (e.g., 2003, 2005, and 2013), there were fewer pixels with low LNS, but the severity of reductions was greater. In areas with little topographic variation, such as the central BDT, there was more spatial variation in low values between years. Positive trends were found predominately in the western and southern Upper Burdekin and southern Belyando basins. Negative trends were most common in the northern Belyando, central Upper Burdekin, and southern Suttor river basins. In total, 79.4 % of the BDT had no significant trend in LNS.
The magnitudes of negative and positive interannual trends in LNS varied substantially between river basins (Fig. 6b, Tables 6 and 7). The Suttor had by far the lowest negative trends (but the largest standard deviation; Table 6). The Upper Burdekin and Cape Campaspe had the least negative trends (Table 6). Positive trends were highest in the Bowen Broken Bogie and lowest in the Belyando (Table 7).
Time series of maps of the Burdekin Dry Tropics from 2000 to 2013 showing
Negative trends in area, interannual rate, and severity of LNS for river basins of the Burdekin Dry Tropics. SD – standard deviation.
Positive trends in area, interannual rate, and severity of LNS for river basin of the Burdekin Dry Tropics. SD – standard deviation.
Some patches of positive and negative LNS trends were found in large areas that spanned multiple river basins (Fig. 6b). These may have been a result of environmental conditions (e.g., low rainfall, soil properties) in some combination other than that used to create the LCCs, or of a single variable not used in the classification that crosses the LCC boundaries, for example more friable soils.
There were strong contrasts in the average LNS of the negative and positive
trend classes between river basins (Tables 6 and 7). The average LNS of
negative trends in the Suttor was nearly twice that of the Upper Burdekin.
The Suttor River basin had most severe LNS reductions in the negative trend
class (Table 6). On average, for negative trends, the Bowen Broken Bogie,
Upper Burdekin, and Lower Burdekin had the least severe reductions in NPP, while the
most severe were in the southern river basins: Belyando, Cape Campaspe, and
Suttor (Table 6). Surprisingly, the Belyando had less severe reductions in
NPP in areas with negative trends (Table 6) than in areas with positive
trends (Tables 7). In the Belyando, the percent LNS for positive trends were
less than
For the entire BDT, the overall spatial distribution of annual hillslope
erosion was strongly correlated (
VAST classes were generally correlated with LNS (Table 8). The average LNS declined with increasing human modification. “Removed” and “bare” had the lowest average LNS of any VAST class. The only negative trend was in the “bare” class, presumably an indication that a small amount of vegetation was present, while “removed” had the largest positive trend. Interannual trends in LNS further differentiated the two classes; “bare” had the only negative trend, while “removed” had the largest positive trend, which may be a result of the strongest regrowth from the lowest starting value of all the classes.
VAST class comparison with interannual trends in LNS and average LNS. SD – standard deviation.
The basis of selection of the reference NPP and detection of anthropogenic reductions in LNS is the classification of the landscape into uniform units (LCCs) with respect to the environmental factors that affect NPP. The procedure was generally successful in creation of classes of environmentally uniform pixels, differing only in the long-term degree of degradation. The same reference sites were frequently selected in multiple, sometimes consecutive, years for the 14 years included in the study and therefore potentially for a longer term. This indicates that degradation, as detected with LNS, corresponded to sites that were persistently below the potential. This emphasized that these sites were not simply subject to some short-term environmental deficiency, such as a single year with spatially patchy lower rainfall. The value of incorporating interannual variation of precipitation in the classification rather than a climatological average is illustrated by the comparison of GLM. UMDLCC proved better able to minimize within-LCC variance while also maximizing the between-LCC variance (Table 1, Fig. 4a and b). The large numbers of UMD reference sites that fell in the VAST “residual” class and the larger reductions in NPP in VAST classes with higher levels of human modification offer further evidence of the reliability of the UMDLCC classification (Table 8). Furthermore, the spatial coincidence of differences in management with differences in LNS found by visual inspection of high resolution imagery suggests that the procedure was able to distinguish regional, anthropogenic land degradation from natural variation in environmental factors.
Similarities between
Nevertheless, undetected errors may arise in the classification process, some of which are noted in the Methods section. Changes in land cover during the study period are unlikely to have caused errors since the rates of pasture clearing decreased dramatically throughout the Burdekin region from 1988 to 2002 and remained relatively low during the study period (2000 to 2012; DSITIA, 2014). A more fundamental problem might arise because the classification procedure did not allow for any interactions between environmental factors in different parts of the study area. A possible example of this from the BDT is the location of the largest spatial variations in LNS and its interannual trends near the coastline (e.g., Lower Burdekin and Bowen Broken Bogie), where rainfall is highest. This is an example of a drawback of statistical classification which can only account for additive effects of the environment, whereas, for example, moisture availability can alter the response of production to management (Ibrahim et al., 2015), possibly nonlinearly. This points to an advantage of replacing the statistical derivation of LCCs with a process-based model that can convolve the environmental factors in realistic mechanisms. Such a model run in “potential” mode, which is without any anthropogenic effects, could create a reference NPP for each pixel. At the present time, however, the environmental variables and parameters needed for a useful process model are only rarely available.
Across the entire BDT region, from 2000 to 2013, the average annual
reduction in NPP below the reference was 2.14 MgC m
Overall, positive temporal trends in LNS were twice as common as negative trends (Tables 6 and 7). The “non-degraded with no trend” class had the largest total area (65.3 %). This class was widespread in every river basin, indicating that most of the BDT region was not affected by severe degradation. In other areas, for example in Belyando and Bowen Broken Bogie, the average LNS of “degraded with positive trends” areas suggests that significant areas were recovering from earlier degradation (Table 7). Nevertheless, some areas were degrading between 2000 and 2013, and in some their negative trends intensified through the study period, as indicated by the extent of the “degraded with negative trends” class (Table 2). Areas classified as “degraded with negative trends” occupied 24.7 % of the entire BDT-candidate areas for actions to reverse or at least arrest the trend. There were a few instances of “degraded with no trends”, a possible indicator sites in a state of long-term, maybe permanent, irreversible degradation or approaching this state. Permanent degradation is a serious condition since it is generally reversible only with intensive remediation (Prince, 2002; Reynolds et al., 2007), which often costs more than the value of the restored land; however, there were a few areas of “degraded with positive trends” which may be examples of land that has been rehabilitated.
While substantial reductions in NPP were found across the BDT region as whole, there was considerable variation between river basins. The link between anthropogenic disturbance and rates of degradation (detected here by low LNS) has been noted by Hill et al. (2005) and Kairis et al. (2015) and specifically in the BDT by McKeon et al. (2009). Independent evidence for anthropogenesis presented here includes correlation with the VAST map which, although not a map of vegetation degradation, does distinguish varying degrees of human-related modification of native vegetation (Thackway and Lesslie, 2005). The good agreement of ranks of average LNS and the VAST classes (Table 8) is evidence that LNS was able to separate human-related degradation from natural variation, at least up to the end of the period of time used for the VAST map (i.e., 2011). In addition, there was qualitative evidence from visual inspection of high resolution remotely sensed imagery, such as abrupt differences across station boundaries (e.g., Fig. 5b, c) and coincidences of visible disturbance around livestock water points. The relationship between degradation, accelerated rates of erosion, and reduced vegetation cover is well known (Lal, 2001) and erosion is the most widespread and recognizable characteristics of land degradation (Ravi et al., 2010), also a primary impact on loss of soil carbon (Rajan et al., 2010). In the present study, there was a strong overall correlation of average LNS with hillslope erosion and gully density (Fig. 7). In the BDT, others have linked erosion with poor grazing management (Bartley et al., 2006) and unsustainable agricultural production (Montgomery, 2007).
Assigning causal relationships to land degradation and natural or anthropogenic factors is difficult due to the close coupling between humans and their environment (Reynolds et al., 2007). The LNS procedure offers one approach that attempts to isolate actual degradation of NPP from less favorable environmental conditions. However, without additional data on land usage, such as livestock numbers and management practices, the causes of the reductions by human-related activities are hard to determine (Bastin et al., 2012). The most commonly cited management practices to reduce degradation are reduction in domestic livestock, reduction in feral herbivores, removal of watering points (Bastin et al., 2012; Fensham and Fairfax, 2008; Silcock and Fensham, 2013), fallowing (Bastin et al., 1993, 2012), or encouraging vegetation that is particularly resistant to overgrazing or able to recover quickly after intense grazing (Bastin et al., 2012; McKeon et al., 2004; Smith et al., 2007). Additional data are needed to interpret low LNS, particularly with field observation.
Given the extremely large areas of provincial, national, regional, and global degradation that are frequently stated (Bai et al., 2008; Bridges and Oldeman, 1999; Kassas, 1995; Oldeman, 1994; UNEP, 1997; Zika and Erb, 2009) and the far-reaching effects of degradation on human livelihoods (Adeel, 2008; UNCCD, 1994), rigorous, quantitative, and objective measurements are urgently needed. While reduction of NPP is a single type of degradation, it is a quantitative measure of the outcome of most forms of degradation relevant to human needs – but not all (e.g., loss of palatable species with no change in NPP; Asner and Heidebrecht, 2005). The widespread occurrence of degradation and its anthropogenic causes and effects require measurements having the large-area coverage and high spatial resolution provided by remote sensing, despite their limitations. LNS is founded on the concept of comparison of the actual conditions with their potential. As noted, there are several weaknesses in the technique that may affect the validity of the results; nevertheless, the fundamental concept of reduction from an explicit standard remains. There also remains a need for improvements in detection of appropriate reference standards, either by local scaling as in LNS or by some other method.
The objectives of the many initiatives to arrest and remediate degradation have been summarized in the concept of zero net land degradation (ZNLD) (Stavi and Lal, 2015). ZNLD seeks to slow current rates of degradation such that the rates of land rehabilitation are, at the very least, equivalent to rates of deterioration (Lal et al., 2012), locally or elsewhere. Achievement of ZNLD depends on comprehensive monitoring to identify land states and trends of degradation. The study presented here used one approach to such regional assessment. While the feasibility of global land degradation neutrality has been debated (Grainger, 2015), the BDT is an example of a region that has seen a reversal of an overall trend toward degradation in productivity.