1. Introduction
Savanna ecosystems cover approximately one-fifth of the Earth’s land surface, extending from tropical to semi-arid regions [
1,
2]. Such areas are characterized by a continuous herbaceous layer with intermittent trees and/or shrubs [
3]. This broad definition covers everything from areas of almost continuous woody cover to areas that are mostly grassland with a few sparse trees [
1,
4]. In light of this variability, savannas are typically defined by the complex interactions between tree and grass layers [
1]. These interactions play an important role in the functioning of savannas by regulating nutrient cycling and resource availability, influencing the biomass and diversity of organisms the savanna can support [
5,
6]. In addition to extensive plant and animal populations, savannas are home to an increasing proportion of the world’s human population as well as the majority of its rangeland and livestock [
1]. They are also among the ecosystems predicted to be the most sensitive to climate change [
7], raising concerns about compounding effects of recent increases in the prevalence of droughts, crop failure, and water scarcity [
8].
Savannas are the dominant land cover type in Africa, covering over half the continent [
1,
2]. While the abiotic template of rainfall, fire, and soil nutrients sets the broad patterns for savanna dynamics and diversity in Africa [
9,
10,
11,
12], biotic influences of humans and wildlife may also strongly influence savanna patterns and processes [
3,
13,
14]. At local scales, herbivory and fire can have as large an effect on savanna dynamics as climate [
3,
15]. Elephants (
Loxodonta africana) have received some of the greatest attention for their ability to influence savanna dynamics due to their large size and conspicuous effects on vegetation [
16,
17,
18,
19,
20]. As a keystone species, elephants exert an impact on the environment disproportionate to their abundance [
21]. They can cause direct mortality of trees when foraging and may increase susceptibility of trees to fire and frost [
22]. Furthermore, elephants may reduce seedling recruitment and promote grass production where trees are removed, as well as altering vegetation structure and nutrient cycling [
22,
23,
24]. Many of these processes play a beneficial role in savannas, making elephants an important part of a healthy ecosystem.
A recent report from a collaboration of international agencies stresses the dire threat to elephant populations in many parts of sub-Saharan Africa due to poaching, with populations in Central and West Africa facing possible elimination [
25]. Poaching levels in southern Africa, however, have been lower than the rest of Africa and elephant populations in many areas have increased steadily since the early 1900s [
25]. Ironically, there have been concerns raised in southern and East Africa about the negative impacts high elephant densities may have on vegetation and other herbivores, the so-called “elephant problem” [
26,
27,
28].
Southern Africa is home to the world’s largest population of African elephants [
29]. These animals serve as the basis for a booming tourism industry, generating jobs and revenue for local communities [
30]. However, they are also a source of human-wildlife conflict, raiding crops and killing people and livestock [
31,
32,
33]. In addition, modification of vegetation structure by elephants may affect other wildlife species positively or negatively, with ramifications for ecosystem sustainability. In systems where elephants promote open savannas, grazers are likely to benefit from increased food availability. For example, buffalo (
Syncerus caffer) appear to prefer grazing in areas recently utilized by elephants in Tanzania and Botswana [
34,
35]. In Kenya’s Tsavo National Park, dramatic reductions in elephant numbers due to poaching led to increasing tree cover and a reduction in grazers such as kongoni (
Acelaphus buselaphus), oryx (
Oryx beisa), and zebra (
Equus quagga) [
36]. While opening of habitats by elephants may benefit grazers, some browser species are likely to be negatively impacted. Bushbuck (
Tragelaphus scriptus) depend on thick cover and the bushbuck population in Chobe National Park, Botswana, declined between the 1960s and 1990s as increases in elephants led to a more open habitat [
37]. Similarly, both lesser kudu (
Tragelaphus imberbis) and bushbuck were eliminated from Amboseli National Park in Kenya as a result of vegetation changes caused by elephants [
38]. Some mixed-feeders, however, like impala (
Aepyceros melampus) and greater kudu (
Tragelaphus strepsiceros) preferentially browse trees with accumulated elephant impact in Chobe National Park [
39,
40]. Similarly, impala and steinbuck (
Raphicerus campestris) preferentially utilize elephant-impacted habitat in Hwange National Park, Zimbabwe [
41]. Managers in elephant-dominated areas need information about the distribution of elephant impacts so that effective management decisions can be made which balance the needs of various wildlife species. Remote sensing indices offer the potential to provide these types of information.
The Moving Standard Deviation Index (MSDI) is a moving standard deviation filter applied to remotely sensed images to assess degradation [
42]. Processes increasing soil heterogeneity can lead to habitat degradation in semi-arid systems [
43]. By expressing the variability in vegetation and soil, the MSDI is used to indicate levels of habitat degradation. Validation studies in semi-arid rangelands of both South Africa and Australia show that areas with higher MSDI values exhibit increased degradation [
42,
44]. This spectral-based degradation assessment technique is an ideal application of remotely sensed data in semi-arid landscapes as it provides continuous and repeatable measures of patterns across the study area and has been shown to operate well in complex regions [
42,
45].
The MSDI is traditionally applied in a 3 × 3-pixel moving window to the red band of remote sensing imagery [
42]. The red band is used due to its inherent correlation with physiological properties of plants, as chlorophyll in plant leaves absorbs red wavelengths of energy along the electromagnetic spectrum. This absorption makes the red band sensitive to variation in both exposed soil and vegetation content, meaning that highly vegetated areas should have low levels of reflection in red wavelengths [
46]. Numerous examples in the literature demonstrate the effectiveness of the red band for detecting vegetation patterns [
46,
47,
48,
49]. Furthermore, combining the red band with a contrasting band that exhibits strong reflection in highly vegetated areas, such as the near-infrared band, produces a reliable metric for total chlorophyll content and changes in leaf pigmentation due to senescence [
50,
51,
52,
53]. Semi-arid landscapes exhibit complex tree-grass-shrub relationships and highly seasonal variation in land cover that are influenced by shifts in rainfall, complicating traditional remote sensing assessments [
54,
55]. The principles outlined above, however, allow such complex and intermingled ecosystems to be studied to detect vegetation change. For example, Archibald and Scholes [
56] utilized the normalized difference vegetation index (NDVI) to assess phenological patterns and vegetation differentiation in African savannas. Such spectral-based measures have been validated in semi-arid environments and show good correspondence with field conditions [
53,
57]. Previous research has shown that NDVI is positively related to elephant densities in African savannas [
58]. This study investigates whether the MSDI allows identification of areas that have been heavily modified by elephants.
Elephants generally exhibit a patchy foraging style, leading to heterogeneity in woody cover that may be identified by the MSDI. Indeed, Nellis
et al. [
59] and Robinson
et al. [
60] applied a similar approach to elephant-impacted habitat in northern Botswana using digitized Space Shuttle photography. While these papers performed only a qualitative validation, they found MSDI values in elephant-modified areas were three to eight times higher than those with relatively undisturbed vegetation in Chobe National Park. Robinson
et al. [
60] call for a quantitative analysis of their findings but, to our knowledge, this has never been completed and the approach has not since been applied to elephants and their impacts.
We address this by evaluating the ability of the MSDI as a means of detecting elephant-modified habitat via remote sensing in the riverfront area of Chobe National Park. We first compare MSDI values at coarse scales between areas with differing elephant utilization intensities and then use quantitative vegetation plots to assess the utility of the MSDI at a fine scale. Alternative covariates that may drive MSDI trends are also investigated. We furthermore assess new approaches to the MSDI by running calculations on vegetation indices and the near infrared band as well as the traditional red band and by varying the window size used in the standard deviation calculation. Chobe National Park presents an excellent opportunity for such a study as it contains high densities of elephants and little vegetation modification by humans. Finding a successful means of detecting elephant impacts via satellite remote sensing will provide the opportunity to monitor changes in elephant utilization of vegetation over time as well as across larger spatial extents than are currently possible. This offers the potential for a better understanding of how elephants change landscapes, informing successful management strategies that can better meet the needs of elephants and other wildlife populations.
4. Discussion
The ability of the Moving Standard Deviation Index (MSDI) to identify elephant-modification of vegetation was assessed in Chobe National Park, Botswana. At a coarse scale, MSDI values were significantly higher in a region with higher elephant utilization, compared to a less utilized region. At a finer scale, focusing on just the highly utilized Chobe riverfront, weighted elephant utilization showed a negative relationship with MSDI values (
Table 1), reversing the coarse-scale trend. Assessment of alternative covariates that could influence the observed patterns suggested that proximity to the Chobe River might be correlated with higher MSDI values (
Table 3). Subset models built on river and inland vegetation plots affirmed that while the negative relationship between MSDI and elephant utilization was generally maintained for the river subset (Supplementary Materials Table S1), it was mostly non-significant for the inland subset (Supplementary Materials Table S2).
Table 3.
Pearson’s correlation coefficients between environmental covariates and Moving Standard Deviation Index (MSDI) values along the Chobe riverfront, Botswana. All dates are in 2012.
Table 3.
Pearson’s correlation coefficients between environmental covariates and Moving Standard Deviation Index (MSDI) values along the Chobe riverfront, Botswana. All dates are in 2012.
Image | Window | Date | Long. | Lat. | Elev. | Dist. Road | Dist. River | Flow Acc. | Flow Dir. | Slope | Veg. Class |
---|
Red | 3 × 3 | 14/4 | −0.150 | 0.357 | −0.590 | −0.254 | −0.492 | 0.061 | 0.024 | 0.218 | 0.337 |
8/5 | −0.149 | 0.304 | −0.543 | −0.286 | −0.471 | 0.047 | 0.032 | 0.226 | 0.314 |
27/7 | 0.003 | 0.419 | −0.594 | −0.238 | −0.456 | 0.026 | −0.025 | 0.165 | 0.310 |
5 × 5 | 14/4 | −0.135 | 0.469 | −0.709 | −0.302 | −0.600 | 0.037 | 0.032 | 0.162 | 0.401 |
8/5 | −0.122 | 0.427 | −0.679 | −0.346 | −0.590 | 0.018 | 0.036 | 0.176 | 0.382 |
27/7 | −0.013 | 0.513 | −0.700 | −0.299 | −0.568 | 0.000 | −0.001 | 0.170 | 0.376 |
7 × 7 | 14/4 | −0.118 | 0.523 | −0.769 | −0.329 | −0.664 | 0.036 | 0.027 | 0.141 | 0.438 |
8/5 | −0.114 | 0.471 | −0.739 | −0.364 | −0.653 | 0.013 | 0.037 | 0.153 | 0.414 |
27/7 | −0.016 | 0.540 | −0.738 | −0.324 | −0.619 | −0.021 | 0.012 | 0.161 | 0.414 |
NIR | 3 × 3 | 14/4 | −0.046 | 0.388 | −0.602 | −0.215 | −0.460 | 0.011 | −0.016 | 0.197 | 0.278 |
8/5 | −0.059 | 0.363 | −0.573 | −0.237 | −0.459 | 0.004 | −0.001 | 0.200 | 0.275 |
27/7 | 0.104 | 0.465 | −0.618 | −0.187 | −0.468 | −0.009 | −0.039 | 0.142 | 0.304 |
5 × 5 | 14/4 | −0.034 | 0.499 | −0.714 | −0.269 | −0.566 | −0.013 | 0.000 | 0.147 | 0.341 |
8/5 | −0.050 | 0.461 | −0.684 | −0.301 | −0.562 | −0.013 | 0.012 | 0.167 | 0.340 |
27/7 | 0.107 | 0.538 | −0.688 | −0.254 | −0.554 | −0.012 | −0.008 | 0.168 | 0.356 |
7 × 7 | 14/4 | −0.044 | 0.528 | −0.751 | −0.284 | −0.616 | −0.019 | 0.007 | 0.130 | 0.369 |
8/5 | −0.060 | 0.497 | −0.731 | −0.316 | −0.620 | −0.017 | 0.020 | 0.148 | 0.376 |
27/7 | 0.096 | 0.558 | −0.713 | −0.278 | −0.600 | −0.023 | 0.017 | 0.170 | 0.386 |
NDVI | 3 × 3 | 14/4 | −0.105 | 0.412 | −0.642 | −0.187 | −0.505 | 0.053 | −0.020 | 0.129 | 0.332 |
8/5 | −0.023 | 0.487 | −0.680 | −0.146 | −0.515 | 0.022 | −0.056 | 0.049 | 0.316 |
27/7 | −0.058 | 0.364 | −0.595 | −0.169 | −0.434 | −0.010 | −0.071 | 0.124 | 0.263 |
5 × 5 | 14/4 | −0.121 | 0.484 | −0.707 | −0.252 | −0.590 | 0.034 | 0.015 | 0.116 | 0.377 |
8/5 | −0.017 | 0.551 | −0.746 | −0.224 | −0.597 | 0.004 | −0.025 | 0.090 | 0.358 |
27/7 | −0.099 | 0.431 | −0.681 | −0.231 | −0.520 | 0.010 | −0.053 | 0.125 | 0.316 |
7 × 7 | 14/4 | −0.137 | 0.509 | −0.729 | −0.294 | −0.631 | 0.033 | 0.024 | 0.114 | 0.399 |
8/5 | −0.025 | 0.575 | −0.773 | −0.265 | −0.643 | 0.009 | −0.010 | 0.084 | 0.380 |
27/7 | −0.116 | 0.455 | −0.710 | −0.270 | −0.559 | 0.002 | −0.043 | 0.117 | 0.345 |
SAVI | 3 × 3 | 14/4 | −0.054 | 0.402 | −0.631 | −0.260 | −0.502 | 0.030 | 0.008 | 0.206 | 0.313 |
8/5 | −0.029 | 0.430 | −0.649 | −0.261 | −0.529 | 0.023 | 0.011 | 0.180 | 0.316 |
27/7 | 0.032 | 0.378 | −0.579 | −0.145 | −0.425 | −0.025 | −0.069 | 0.129 | 0.252 |
5 × 5 | 14/4 | −0.058 | 0.488 | −0.720 | −0.323 | −0.600 | 0.010 | 0.030 | 0.155 | 0.362 |
8/5 | −0.023 | 0.497 | −0.718 | −0.331 | −0.606 | −0.003 | 0.027 | 0.165 | 0.356 |
27/7 | 0.010 | 0.447 | −0.663 | −0.225 | −0.511 | 0.001 | −0.038 | 0.138 | 0.299 |
7 × 7 | 14/4 | −0.076 | 0.505 | −0.743 | −0.346 | −0.642 | 0.008 | 0.036 | 0.139 | 0.383 |
8/5 | −0.043 | 0.510 | −0.744 | −0.347 | −0.649 | −0.001 | 0.036 | 0.144 | 0.376 |
27/7 | 0.001 | 0.470 | −0.687 | −0.254 | −0.543 | −0.002 | −0.023 | 0.139 | 0.323 |
MSAVI2 | 3 × 3 | 14/4 | −0.040 | 0.390 | −0.611 | −0.270 | −0.489 | 0.027 | 0.015 | 0.216 | 0.301 |
8/5 | −0.022 | 0.414 | −0.625 | −0.274 | −0.517 | 0.022 | 0.025 | 0.195 | 0.306 |
27/7 | 0.043 | 0.387 | −0.584 | −0.144 | −0.430 | −0.026 | −0.067 | 0.129 | 0.253 |
5 × 5 | 14/4 | −0.039 | 0.483 | −0.708 | −0.334 | −0.592 | 0.006 | 0.037 | 0.159 | 0.353 |
8/5 | −0.016 | 0.485 | −0.703 | −0.340 | −0.598 | −0.003 | 0.033 | 0.171 | 0.348 |
27/7 | 0.025 | 0.458 | −0.667 | −0.226 | −0.514 | 0.001 | −0.036 | 0.142 | 0.301 |
7 × 7 | 14/4 | −0.057 | 0.499 | −0.732 | −0.356 | −0.635 | 0.006 | 0.041 | 0.142 | 0.374 |
8/5 | −0.034 | 0.500 | −0.731 | −0.354 | −0.642 | −0.001 | 0.041 | 0.147 | 0.367 |
27/7 | 0.016 | 0.476 | −0.688 | −0.253 | −0.544 | −0.002 | −0.019 | 0.141 | 0.322 |
Table 4.
Pearson’s correlation coefficients between environmental covariates and Moving Standard Deviation Index (MSDI) values in the southern comparison area of Chobe National Park, Botswana. All dates are in 2012.
Table 4.
Pearson’s correlation coefficients between environmental covariates and Moving Standard Deviation Index (MSDI) values in the southern comparison area of Chobe National Park, Botswana. All dates are in 2012.
Image | Window | Date | Long. | Lat. | Elev. | Dist. Road | Dist. River | Flow Acc. | Flow Dir. | Slope | Veg. Class |
---|
Red | 3 × 3 | 14/4 | −0.346 | −0.010 | −0.366 | −0.185 | −0.342 | 0.062 | −0.028 | 0.031 | 0.018 |
8/5 | −0.384 | −0.059 | −0.314 | −0.110 | −0.383 | −0.026 | −0.034 | 0.019 | −0.014 |
27/7 | −0.300 | 0.042 | −0.218 | −0.136 | −0.321 | −0.022 | −0.035 | 0.037 | −0.006 |
5 × 5 | 14/4 | −0.450 | −0.005 | −0.416 | −0.182 | −0.449 | 0.013 | −0.054 | 0.007 | 0.010 |
8/5 | −0.477 | −0.040 | −0.377 | −0.152 | −0.479 | −0.033 | −0.058 | 0.010 | −0.018 |
27/7 | −0.395 | 0.096 | −0.310 | −0.202 | −0.427 | −0.036 | −0.027 | −0.021 | 0.010 |
7 × 7 | 14/4 | −0.491 | −0.017 | −0.444 | −0.172 | −0.485 | −0.011 | −0.072 | 0.005 | 0.011 |
8/5 | −0.526 | −0.039 | −0.401 | −0.140 | −0.525 | −0.054 | −0.088 | 0.005 | −0.031 |
27/7 | −0.485 | 0.078 | −0.366 | −0.230 | −0.517 | −0.062 | −0.056 | −0.027 | −0.004 |
NIR | 3 × 3 | 14/4 | −0.285 | −0.093 | −0.210 | −0.055 | −0.270 | −0.031 | −0.028 | −0.038 | 0.027 |
8/5 | −0.296 | −0.100 | −0.233 | −0.071 | −0.291 | −0.065 | −0.033 | −0.006 | 0.017 |
27/7 | 0.135 | 0.248 | −0.120 | −0.214 | 0.108 | −0.035 | 0.070 | 0.092 | 0.121 |
5 × 5 | 14/4 | −0.422 | −0.082 | −0.296 | −0.093 | −0.413 | −0.067 | −0.071 | −0.023 | 0.029 |
8/5 | −0.430 | −0.113 | −0.307 | −0.122 | −0.428 | −0.062 | −0.083 | 0.001 | 0.036 |
27/7 | 0.116 | 0.382 | −0.167 | −0.268 | 0.068 | −0.016 | 0.060 | 0.072 | 0.165 |
7 × 7 | 14/4 | −0.489 | −0.082 | −0.345 | −0.121 | −0.478 | −0.062 | −0.100 | −0.007 | 0.015 |
8/5 | −0.506 | −0.120 | −0.342 | −0.122 | −0.501 | −0.084 | −0.086 | 0.002 | 0.035 |
27/7 | 0.095 | 0.432 | −0.181 | −0.278 | 0.040 | −0.017 | 0.033 | 0.030 | 0.144 |
NDVI | 3 × 3 | 14/4 | −0.101 | 0.292 | −0.357 | −0.259 | −0.129 | 0.119 | 0.000 | 0.076 | 0.063 |
8/5 | −0.203 | 0.215 | −0.307 | −0.238 | −0.235 | 0.060 | 0.022 | 0.064 | −0.002 |
27/7 | −0.186 | 0.101 | −0.218 | −0.190 | −0.212 | 0.019 | −0.020 | 0.048 | 0.010 |
5 × 5 | 14/4 | −0.141 | 0.344 | −0.396 | −0.266 | −0.172 | 0.073 | −0.002 | 0.035 | 0.079 |
8/5 | −0.229 | 0.264 | −0.341 | −0.255 | −0.264 | 0.036 | −0.003 | 0.018 | −0.021 |
27/7 | −0.272 | 0.104 | −0.290 | −0.232 | −0.303 | −0.008 | −0.028 | 0.010 | 0.019 |
7 × 7 | 14/4 | −0.115 | 0.357 | −0.388 | −0.240 | −0.138 | 0.050 | −0.005 | 0.022 | 0.084 |
8/5 | −0.217 | 0.293 | −0.338 | −0.251 | −0.254 | 0.027 | −0.021 | 0.014 | −0.034 |
27/7 | −0.340 | 0.118 | −0.353 | −0.277 | −0.370 | −0.017 | −0.033 | −0.009 | 0.008 |
SAVI | 3 × 3 | 14/4 | 0.043 | 0.306 | −0.239 | −0.177 | 0.015 | 0.160 | −0.019 | 0.072 | 0.074 |
8/5 | −0.092 | 0.249 | −0.253 | −0.245 | −0.121 | 0.135 | 0.012 | 0.070 | 0.029 |
27/7 | −0.102 | 0.089 | −0.204 | −0.169 | −0.116 | 0.012 | −0.009 | 0.058 | 0.044 |
5 × 5 | 14/4 | 0.027 | 0.391 | −0.299 | −0.211 | 0.001 | 0.101 | −0.019 | 0.042 | 0.097 |
8/5 | −0.083 | 0.318 | −0.290 | −0.283 | −0.115 | 0.087 | 0.015 | 0.007 | 0.022 |
27/7 | −0.133 | 0.116 | −0.247 | −0.211 | −0.153 | −0.016 | −0.008 | 0.034 | 0.068 |
7 × 7 | 14/4 | 0.051 | 0.406 | −0.295 | −0.170 | 0.035 | 0.068 | −0.011 | 0.031 | 0.099 |
8/5 | −0.051 | 0.354 | −0.277 | −0.268 | −0.084 | 0.071 | −0.010 | 0.005 | 0.005 |
27/7 | −0.158 | 0.151 | −0.293 | −0.252 | −0.176 | −0.023 | −0.014 | 0.014 | 0.059 |
MSAVI2 | 3 × 3 | 14/4 | 0.068 | 0.326 | −0.246 | −0.174 | 0.041 | 0.135 | −0.010 | 0.079 | 0.075 |
8/5 | −0.044 | 0.262 | −0.237 | −0.240 | −0.073 | 0.140 | 0.021 | 0.051 | 0.037 |
27/7 | −0.055 | 0.112 | −0.206 | −0.171 | −0.068 | 0.017 | 0.013 | 0.075 | 0.064 |
5 × 5 | 14/4 | 0.066 | 0.405 | −0.275 | −0.202 | 0.042 | 0.084 | −0.030 | 0.046 | 0.102 |
8/5 | −0.019 | 0.337 | −0.255 | −0.285 | −0.051 | 0.082 | 0.011 | 0.006 | 0.050 |
27/7 | −0.080 | 0.140 | −0.239 | −0.221 | −0.097 | −0.005 | 0.007 | 0.050 | 0.092 |
7 × 7 | 14/4 | 0.074 | 0.428 | −0.296 | −0.173 | 0.058 | 0.055 | −0.012 | 0.041 | 0.119 |
8/5 | 0.014 | 0.374 | −0.246 | −0.272 | −0.016 | 0.083 | −0.004 | 0.004 | 0.031 |
27/7 | −0.100 | 0.175 | −0.280 | −0.240 | −0.116 | −0.028 | −0.005 | 0.024 | 0.085 |
These varying results may reflect the ways in which elephants influence landscapes across multiple scales. Kriging results affirm that elephant utilization of vegetation is patchy (
Figure 6, Supplementary Materials Figure S4), but patches of utilization can be relatively large, coving 130–360 ha [
120]. Thus, at a coarse scale elephant impacts increase the overall heterogeneity of land cover, resulting in our observation of higher MSDI values in regions with greater utilization by elephants. This is likely why previous studies suggested that higher levels of MSDI correspond to increasing modification of vegetation by elephants [
59,
60]. These studies used a coarser assessment than we did, with 667 m pixels [
60] as compared with our 250 m MODIS pixels. At finer scales, however, our spatial regression analyses reveal a negative relationship between MSDI values and elephant utilization. This indicates that areas showing high elephant utilization tend to show greater homogeneity (lower standard deviation) than those with less utilization. While this is not apparent across an entire landscape, it is likely to be valid within a patch heavily utilized by elephants. Studies in southern Africa have shown increases in shrubs under growing elephant numbers, often leading to dense, fairly homogenous, shrub layers [
64,
120,
121,
122]. Thus, elephant utilization may lead to increased homogeneity within patches through removal of large trees and promotion of more uniform shrub forms, resulting in lower MSDI values.
A homogenizing influence of elephants is likely to be especially strong near the Chobe River, which is known to influence elephant utilization of vegetation in the park [
65], and was emphasized in our findings. The river provides the primary source of water in the dry season and is used heavily by elephants due to their high water requirements. This area has scare vegetation and large amounts of bare soil, due in part to extensive trampling by large herbivores (
Figure 7). Such an area will contrast strongly with nearby vegetation and water, resulting in very high MSDI values along the river’s edge (
Figure 2 and
Figure 3, Supplementary Materials Figures S1–S3). While large amounts of bare soil have been considered synonymous with high levels of elephant impact [
59], we feel this oversimplifies the situation. Our study attempts to detect modification of vegetation structure by elephants, not just areas where all plants have been removed. By definition, this imposes some limitations on our assessment.
The thin riparian fringe along the river’s edge has a long history of high utilization, reducing the prevalence of large trees [
64]. The way our data were collected, this absence of trees results in low records of utilization, helping explain why the highly utilized river’s edge predicts relatively low levels of utilization. This is demonstrated by the kriged maps generated from our field data, which show lower utilization right along the river’s edge and increased utilization beyond this where woody vegetation is plentiful but distances to water are still moderate (
Figure 6, Supplementary Materials Figure S4).
Previous studies using the MSDI assessed only the standard deviation of the red band [
42,
44,
123,
124]. Because our interest was in assessing modification of vegetation, we expanded this to consider MSDI values calculated on the near infrared (NIR) band, as well as on three vegetation indices used to assess savanna vegetation. Interestingly, all of our models showed similar trends regardless of the band or index used to calculate the MSDI (
Table 1). Also, predictions across the riverfront using universal kriging offered similar results for all bands and indices (
Figure 6, Supplementary Materials Figure S4). These similarities likely result from the MSDI’s emphasis of variability across the landscape rather than direction of pattern. Soil and vegetation show contrasting patterns of reflection in red and NIR bands, which are picked up by the MSDI. The three indices considered are derived from various combinations and transformations of the red and NIR bands (see Equations (1)–(3)), thus areas with patches of soil and vegetation should show higher standard deviations whether one is considering the bands or indices.
Figure 7.
The river’s edge in Chobe National Park, Botswana, is highly utilized by elephants and other species in the dry season. Extensive trampling results in scare vegetation and large amounts of bare soil, influencing Moving Standard Deviation Index (MSDI) values for this area. Photo credit: T. Fullman.
Figure 7.
The river’s edge in Chobe National Park, Botswana, is highly utilized by elephants and other species in the dry season. Extensive trampling results in scare vegetation and large amounts of bare soil, influencing Moving Standard Deviation Index (MSDI) values for this area. Photo credit: T. Fullman.
Our work also departed from previous studies by investigating the effect of changing the window size used in the standard deviation calculation. Previous studies used a 3 × 3-pixel window [
42,
44,
123,
124], and we added 5 × 5- and 7 × 7-pixel window sizes. Our analyses showed that while results were generally similar to smaller window sizes, a 7 × 7-pixel window resulted in lower model accuracy. Whether the 3 × 3- or 5 × 5-pixel window size offered better error reduction and explanatory power depended on the band or index used in the calculation. Interestingly, our evaluation showed that for the red band, a 5 × 5-pixel window size offered slightly better performance than the 3 × 3-pixel window used in previous studies. While it is unlikely that the general relationships highlighted in previous studies using a 3 × 3-pixel window on the red band would change if assessed with a 5 × 5-pixel window, our findings nonetheless suggest that future MSDI studies on the MODIS red band include a 5 × 5-pixel window along with the traditional 3 × 3.
While there is a statistically significant relationship between the MSDI and elephant utilization, the explanatory power of this relationship is relatively low, as indicated by the low R
2 values (
Table 1). Comparison between the ordinary and universal kriging models agree with this, showing similar R
2 and RMSE values between models with and without the effects of the MSDI considered (
Table 2). This suggests that while a relationship exists between MSDI values and elephant utilization, there may be other factors also influencing the MSDI. The presence of herbaceous vegetation may be one such factor. Vegetation indices, such as those used here in calculation of the MSDI, provide information about primary productivity but do not directly distinguish between reflectance from woody and herbaceous vegetation without additional information considering phenological shifts [
46,
86]. In light of this, the presence of herbaceous vegetation underneath a tree canopy may mask the changes in woody vegetation structure in which we are interested. This is likely another reason why the edge of the Chobe River, where both woody and herbaceous cover has typically been removed (
Figure 7), showed such high MSDI values. The moderate spatial resolution of MODIS pixels at 250 m may also have influenced our results. The MSDI appears most suited to demonstrate coarse-scale trends reflecting increased heterogeneity due to patchy foraging across a landscape. Identifying finer-scale patches of high elephant use, however, may require imagery at finer spatial scales. Previous attempts to detect elephant impacts with remotely sensed methods have often used finer-scale imagery [
125,
126] or aerial photography [
64,
120]. While Scan Line Corrector issues with the Landsat 7 ETM+ satellite prevented evaluation of the MSDI using Landsat imagery, there is hope that with the newly launched Landsat 8 satellite, such analyses may be possible in the future. The cost of such finer-scale analyses will likely be in the extent that they can be generalized to inform regional management. Because of the importance of managing elephants not just in Chobe but across the entire Kavango-Zambezi Transfrontier Conservation Area, methods should ideally be developed that link findings from finer resolution images with coarser images like MODIS to provide information on regional patterns. In the meantime, while the MSDI provides information about elephant utilization, it does not seem to have a strong enough pattern to be effective for monitoring elephant impacts.