Research Article, Geoinfor Geostat An Overview Vol: 4 Issue: 3
Responses of Vegetation to Temperature Gradients around Geothermal Features: A Review on Wairakei -Tauhara Geothermal Field, Taupo, New Zealand
| Abdul Nishar*, Dan Breen, Grant Lawrence and Barbara Breen* | |
| School of Applied Sciences, AUT University, Auckland, New Zealand | |
| Corresponding authors: Abdul Nishar School of Applied Sciences, AUT University, Auckland, New Zealand E-mail: abdul_nishar@xtra.co.nz Barbara Breen School of Applied Sciences, AUT University, Auckland, New Zealand E-mail: barbara.breen@aut.ac.nz |
|
| Received: April 12, 2016 Accepted: June 14, 2016 Published: June 23, 2016 | |
| Citation: Nishar A, Breen D, Breen B (2016) Responses of Vegetation to Temperature Gradients around Geothermal Features: A Review on Wairakei -Tauhara Geothermal Field, Taupo, New Zealand. Geoinfor Geostat: An Overview 4:3. doi:10.4172/2327-4581.1000147 |
Abstract
Geothermal ecosystems experience extreme conditions but can support unique communities of organisms. This study uses historical thermal infrared images and aerial photographs to review the vegetation responses to temperature gradients around geothermal surface features in the Wairakei-Tauhara geothermal field. Here, the spatial distribution of ‘‘geothermal kanuka’’, Kunzea tenuicaulis and related species and hybrids is mapped in relation to ground temperatures measured from the thermal infrared images. Optimal growing conditions for these plant communities in the geothermal area were at ground temperatures higher than ambient. Areas of moderate to high surface heat continued to support plant communities but as ground surface temperature reduced, vegetation growth and establishment increased. The results presented here demonstrate the impact of surface temperature on vegetation and suggest that long-term temperature intensification or reduction over an area does not wipe out vegetation completely. In this case, geothermal kanuka was able to adapt to changes in the temperature and increase its distributing. Understanding how these plants survive in high-temperature ecosystems may provide insight into how they cope with changes in temperature in these and other extreme habitats and how other species may respond to future climate change. An awareness of the interactions between temperature and plant community structure can help plan conservation strategies for the future.
Keywords: Climate change; Geothermal; GIS; Remote sensing; Taupo; Thermal infrared
Keywords |
|
| Climate change; Geothermal; GIS; Remote sensing; Taupo; Thermal infrared | |
Introduction |
|
| Geothermal features in New Zealand host distinctive assemblages of plants that survive under extreme geophysical and geochemical conditions [1-4]. Steep temperature gradients, elevated levels of gases and minerals and low pH create harsh but specialised niches for unique bacteria, algae, plants and animals [5,6]. Despite their ecological significance [1,7], only a few studies have examined the relationship between these environments and their unique biota Boothroyd [6]. Winterbourn and Brown [8] observed that the distribution of the fauna around streams in the Taupo region was related to the geothermal heat. Duggan et al. [9] concluded that geothermal heat had a major influence on the distribution and composition of biotic communities. | |
| Some studies noted the special characteristics of plant communities in these environments [10,11] but few explore in detail the relationship between geothermal factors and their impact on vegetation. van Manen and Reeves [12] studied the response of Kunzea ericoides var. microflora to changes in ground temperature and the risk of invasive species in areas with lower ground temperatures. Burns [5] looked at vegetation patterns near the TeKopia steam field near Taupo and concluded that soil temperature had the greatest influence on vegetation composition and structure. Similarly, Given [1] at Karapiti, Glime and Iwatsuki [13] at Ponponyama (Japan), Sheppard [14] at Yellowstone National Park (U.S.A.), and Broady et al. [15] at Mt Melbourne (Antarctica). All found the zonation of geothermal vegetation to be closely related to soil temperature. | |
| Soil temperature has fundamental effects on the abiotic and biotic processes determining the distribution and density of geothermal vegetation [16-19]. Soil temperature is a critical factor controlling the physiological activity and growth of plants [20], restricting root growth and reducing nutrient uptake [21], and also affects root permeability and water uptake [22]. Moreover, soil temperature influences soil moisture levels [23] and microbial function and productivity [24,25]. | |
| Geothermal heat signatures are linked to crustal geology and tectonic activities. They are often unevenly distributed [26] and may change with time [27]. The levels of soil heat, steam and gaseous output vary [28,29] depending on geological structures and the depth of the magma chamber and the watertable [28]. The area of soil heat emissivity will depend on the amount of heat flowing [30] and can create a hostile environment for plants. The impact on vegetation reflects its distance from a heat source point [31]. It is widely agreed that the effect of soil heat, irrespective of its source, is a major determinant of vegetation growth around the world [1,32,33]. | |
| Remote sensing of thermal infrared radiation is commonly used to measure land surface temperature, its distribution and temporal variation [34,35] and has often been used to detect geothermal activity [36,37]. Remote sensing provides data with a wide coverage of the area of interest, which is especially useful for geothermal exploration and research given the scattered occurrence of surface features [38-40]. | |
| In addition, thermal infrared remote sensing has been applied to the classification and long-term monitoring of geothermal ecosystems [40,41] with research indicating a negative correlation between land surface temperature and vegetation [42-44]. Increasing emphasis has been placed on understanding this relationship [45,46]. | |
| This study aims to use GIS and remote sensing techniques to assess variations in the spatial distribution of Kunzea tenuicaulis from historical aerial photographs taken in different years. The spatial distribution change pattern were compared with the changes in geothermal heat signatures seen in the TIR imagery, demonstrating the level of change driven by the ground heating caused by geothermal features. The level of impact caused by the geothermal ground heating will also give an insight of the influence climate change can have on vegetation. | |
Methods |
|
| Study area | |
| The Taupo Volcanic Zone (TVZ) is an area of intense geothermal activity in the North Island of New Zealand [47,48]. It covers an area approximately 30 kilometres wide by 150 kilometres long and contains 23 geothermal fields or systems [49]. The structural association of TVZ geothermal fields was examined by Wood [50], who concluded that the majority of the fields were located at the margins of major volcanic craters. The TVZ contains all but one of New Zealand’s geothermal systems [51]. The 23 geothermal systems identified within the TVZ [49] have different heat output [48], ranging from <1 MW (Motuoapa geothermal system) to 540 MW (Waiotapu geothermal system) [49]. | |
| Each geothermal field is typically 5 to 25 km2 in area [49] and it has been noted that the TVZ geothermal systems appear to be regularly spaced, with an average separation of about 15 km [52,53]. Due to its suitability for power generation, the TVZ has received previous attention by investors [54-56]. Based on geology, [57] estimate the age of Wairakei-Tauhara geothermal field to be around 0.5 Ma at a minimum. The Wairakei-Tauhara geothermal field appears to have maintained activity during recent volcanism [58] and has the largest observed heat flows in the TVZ [48]. | |
| The section of Wairakei-Tauhara geothermal field covered by this study (Figure 1) is referred to as the Crown Road geothermal area, covering an area of about 1 km2. This ‘horseshoe ’shape of geothermal features with hot spots and theheated ground area is known for its steaming ground. It is protected against commercial development and owned by the Tauhara Middle 15 Trust who have consented to this study being conducting on their land. | |
 |
Figure 1: Taupo Volcanic Zone - Geothermal fields. (Data source: Waikato Regional Council and Taupo District Council). 1a. Location of the TVZ in New Zealand. 1b. geothermal fields in the TVZ. 1c. The Wairakei - Tauhara geothermal field, the hot ground boundaries allocated by Taupo District Council and the study site. 1d. Aerial photo of the study area. |
| Study species | |
| The dominant plant species found in the study area is ‘‘prostrate kanuka’’ or ‘‘geothermal kanuka’’ (Figure 2a and 2b). This plant belongs to the native tea tree genus Kunzea. While previously referred to as Kunzea ericoides var. microflora, in publications on geothermal vegetation [12,59-61], a recent revision of the Kunzea ericoides (Myrtaceae) complex by de Lange [62] recognises ten species, all endemic to New Zealand, seven of which are new species. The revised species of Kunzea found around geothermal areas is Kunzea tenuicaulis [62] and is recognised by “a combination of growth habitat, branchlet hair and floral characters, supplemented by cytological and molecular differences” as detailed in de Lange [62-65]. The New Zealand Threat Classification System [66] recognises Kunzea as a species under risk due to distribution confined to the active geothermal areas of the TVZ [12]. | |
|
Figure 2: Photographs showing prostrate kanuka.2a.The distribution of prostratekanuka around a feature surface feature.2b.Close-up view. |
| Resample of aerial imagery | |
| In this study, the historical ortho- images had a different spatial resolution (Table 1); because the spatial resolution is related to the altitude, focal length and resolution of the camera [67], which improved with advancement in camera technology. The aerial imagery was supplied by the local council. All the imager was captured with within the summer months to reduce the sun shadows effect. In summer, the sun is at its highest peak in the sky and therefore the sun shadows will be very small. Otherwise imagery taken at other times in the year, would have large areas of dark spots and the images would be unclear within those shadows. | |
|
Table 1: Resolution of aerial imagery captured in different years. |
| Resampling was introduced to improve the amount of information that can be extracted from imagery, but in this case, the imageries were degraded or down-sampled [68] to simulate pixel resolutions of the earliest imagery used in this study. Aerial photos are continuous data. Each pixel represents the response of a region of a sensor to light directed at it and as that light varies, the response varies continuously. The result is usually discretized (often into 255 or 256) categories, but that does not change the nature of the data. Therefore, you want to interpolate rather than using categorical algorithms like nearest neighbour or majority. Bilinear interpolation is usually just fine; at some cost in execution time, cubic convolution will retain local contrast a tiny bit better. A small amount of additional blurriness is unavoidable, but that is almost impossible to notice until the image has undergone many such transformations. The errors made with the nearest neighbor are much worse in comparison. Resampling for this study was done using Python’ program language. The tool (Figure 3) downsampled 2002, 2008 and 2012 imagery to match the 1999 imagery resolution using interpolate technique. The tool can be run from within ArcGIS as a script, or from the command line by supplying the required input parameters at run time. | |
|
Figure 3: Python script used for resampling 2012, 2008 and 2002 aerial imagery to the resolution of the 1999 imagery. |
| Changes in the vegetation pattern with time | |
| To investigate changes in the spatial distribution of Kunzea tenuicaulis over time, aerial photographs from 1999, 2002, 2008 and 2012 were classified in ENVI 5.2 to extract the areas of Kunzea tenuicaulis in each year’s aerial imagery. The supervised classification workflow in ENVI was used to firstly define training areas for different vegetation types and then classify the remaining areas using the maximum likelihood method. Images were then smoothed to remove specking before saving the classification images in vector (shapefiles) and raster formats. The same classification was used to identify vegetation in images from 1999, 2002, 2008 and 2012. The classified vector datasets were imported to ArcMap 10.3.1 for further analysis and display. Cover of vegetation was compared among years and field validation conducted to verify all known areas of Kunzea tenuicaulis. Field validation was conducted in the summer of 2015, same time of the year the imagery’s were captured to disregard any inconsistent area of distribution pickup during the analysis. Furthermore, linear regression analysis was used to test for relationship between time and vegetation distribution. Statistical analyses and graphics were performed using R version 3.2.2. (R Core Team, 2015). | |
| Alterations in geothermal land surface temperature with time | |
| The thermal infrared imagery from 2009 and 2014 was compared using ArcGIS 10.3. To display variation between cooler to warmer temperatures, a percent clip linear stretch colour ramp was used to categorise pixels into ten intervals between the minimum and maximum values. The stretched renderer works well where there is a large range of values to display, such as in imagery, aerial photographs, or elevation models. The same colour ramp and stretch were used for both thermal raster’s. | |
| The thermal infrared imagery was provided by a local geothermal energy company, who have been capturing thermal infrared imagery for their monitoring requirements since the 1980’s. The high quality of data required for this comparative study restricted the use of thermal infrared imagery captured prior to 2009. Also, not knowing the specifications of the camera used introduced calibration issues. The thermal infrared remote sensing data obtained did not allow for any further classification or estimation of the surface temperature. Although; the thermal infrared imagery is captured at night to reduce effects of sunlight on the heat contrasts, the flights are scheduled in summer months for favourable flying conditions. | |
Results |
|
| Geothermal land surface temperature | |
| The thermal data collected from the Crown road geothermal although a ‘snapshot’ of the land surface temperature at the time, clearly shows the geothermal footprints and surface heat dispersion. Geothermal features are evident by their deep red colour. Field validation indicated that these geothermal features are associated with steaming heated ground and not discharging geothermal springs or streams. The areas in yellow are warm despite an absence of features, with the level of soil heat emissivity depending on the level of heat flowing Hoang [30] and the distance from the heat source [28,31]. The yellow to greenish areas emit low heat but are still above ambient surface temperatures. The 2009 and 2014 thermal datasets (Map 1, Figure 4) differ in that for 2009 the majority of the study area is within the mid to high surface temperature range, with only small patches of low surface temperature. In the 2014 TIR dataset, however (Map 2, Figure 4), the majority of the study area is within the mid to low surface temperature range, with relatively small patches of high surface temperature. Most importantly, steaming grounds being indicators of geothermal heat up flow [69] has significantly (59 percent) reduced from 2009 to 2014. | |
|
Figure 4: Variation in ground temperature at the Crown Road geothermal area in 2009 and 2014. |
| Changes in the spatial distribution of Kunzea tenuicaulis | |
| The distribution of Kunzea tenuicaulis varies from year to year. From 1999 to 2002, there is an increase in the extent of Kunzea tenuicaulis in the study area (Figures 5 and 6). In the 2002 aerial imagery, the range of Kunzea tenuicaulis not only expands outward from already established areas but also spreads to other geothermal areas where no Kunzea tenuicaulis was detected in 1999. From 1999 to 2002 there was a 20 percent increase in the extent of Kunzea tenuicaulis in the study area. Between 2002 and 2008, Kunzea tenuicaulis extended to sites not populated before. From 2002 to 2008 there was a 17 percent increase in the extent of Kunzea tenuicaulis. From 2008 to 2012, there is a larger (70 percent) increase in the extent of Kunzea tenuicaulis in established areas and also at a few new sites. | |
|
Figure 5: Change in spatial distribution of Kunzea tenuicaulis. Fig 5a spatial distribution of Kunzea tenuicaulis, 1999. Fig 5b spatial distribution of Kunzea tenuicaulis, 2002. Fig 5c spatial distribution of Kunzea tenuicaulis, 2008. Fig 5d spatial distribution of Kunzea tenuicaulis, 2012 |
|
Figure 6: Increase in Distribution of Kunzea tenuicaulis,Crown Road Geothermal Area 1999-2012. |
Discussion |
|
| Understanding long-term environmental change ideally requires studying time series of areas through time [70]. Before the introduction of GIS, vegetation was mapped manually from aerial photographs [71,72], often for repeated surveys [73-76]. However, A Chrono-sequence approach [77] using aerial imagery and remotely sensed data analysed with Geographic Information Systems (GIS) technology [78,79] is a pragmatic approach to this task [80]. The advantage of using aerial photography is that it provides a detailed, consistent and permanent time series which can be analysed by a range of independent and emerging technologies, analyses and variables in a rapid systematic manner [12,80]. However, only recently has it been applied to map geothermal features and associated vegetation [12]. | |
| The review revealed that the spatial distribution of Kunzea tenuicaulis has been increasing from 1999 to 2012. The study area shows a constant increase in the size of Kunzea tenuicaulis in every aerial imagery captured. The increase is far too significant and increment to assume that it a caused by normal vegetation dynamics [81,82]. This level of change in vegetation pattern and the unexpected responses is a strong indicator of a change in environmental variable [83,84]. In geothermal fields, vegetation establishment and growth is limited by several chemical and physical factors but strongly controlled by the thermal gradient [85], which is one of the most important parameters affecting land surface characteristics [86-89]. | |
| Furthermore, thermal infrared remote sensing data was used show changes to the thermal characteristics of the study area. This was possible because the thermal contrast between cold and hot land surfaces is high. Comparing thermal infrared remote sensing data from 2009 and 2014 data revealed that there was a significant drop in surface heat. The geothermal features have maintained their shape but the area of high heat has reduced. The major change in surface heat is seen around the geothermal features, majority of the mid temperate area are now within the warm temperature range. | |
| The reduction in geothermal surface heat and Kunzea tenuicaulis distribution show a negative correlation. This highly suggests that the change in the geothermal heat and reduction in land surface temperature (LST) over the study area is influencing Kunzea tenuicaulis boost. Due to a decrease in the LST, the soil conditions turned favourable to allow Kunzea tenuicaulis to thrive in once a very hostile ecosystem. A change like this in the LST would assist in increasing soil respiration rates [90,91] reflecting changes in various soil biological properties. A reduction in LST will also assist in soils moisture retention [92]. Furthermore; because soil moisture directly limits the rate of Nitrogen mineralization, with better soil moisture retention, the soil in the study area will have more Nitrogen for uptake by vegetation during the growing season [93,94], boosting further development. The drop in LST did not bring the temperature to the ambient level but better still brought it to a more tolerable range allowing Kunzea tenuicaulis to increase in distribution, which is a great indicator of vegetation intensification and growth [95,96]. The drop in LST has been gradual and progressive from 1999 to 2012 due to which the increase the distribution and density of Kunzea tenuicaulis has followed an uninterrupted upward trend without any stress or damaging effects [97]. The adverse ecological effects of temperature on vegetation have been noted by many researchers [98-100] but for a change, it is reassuring to see that an alteration in LST is proving beneficial for vegetation growth and development. | |
| Moreover, knowing the trend of Kunzea tenuicaulis and geothermal LST beyond the time frame of this study would have provided a wider understanding of the long term effects. Aerial photographs of the study site were obtained from the local council and since the study site is on outskirts of the Taupo town, it was not always covered in the local council’s aerial imagery capture scope. In addition, regular thermal infrared data capture is not common due to cost and limited use. Therefore, unconventional methods of thermal infrared data capture are being introduced [101]. Currently in New Zealand, aerial or thermal infrared imagery standard does not exist. An imagery capture standard will provide a minimum data specification, promoting ahigh quality of data capture. | |
| Furthermore, geothermal land surface heating simulators global warming and climate change conditions [16,102-106]. At present, various climate change models are used to investigate the impact of global warming and climate change [107,108]. Over the years, these models have become better and more reliable [109]. However, predictions of climate change based on such models largely ignore climate – ecosystem interactions [110] and limit the ability to simulate the climate change in smaller areas [111,112]. Geothermal land surface heating as simulators incorporate climate – ecosystem interactions previously ignored [110]. A singular unit of geothermal hotspots are localised [113,114] and is ideal to simulate climate change in smaller areas. Geothermal land surface heating have been naturally occurring for a long time [57,115-117] due to which, establishing climate change predictions on their characteristics, has a higher degree of reliability. We know that with climate change and global warming; the vegetation community will go through changes and this study assists in comprehending the level of change that could be expected. | |
Conclusion |
|
| Geothermal areas are of great economical and geological importance, despite being exposed to tough conditions is sensitive to alterations to the unique physical and chemical characteristics of geothermal ecosystems. Much is known regarding the response of plants to the direct effects of surface temperature, but the use of a time serial aerial and the thermal infrared allowed for comparisons between surface temperature changes and plant growth through altering soil condition. The aerial and thermal infrared capture programs in New Zealand is driven by corporation organisation due to which the coverage and flights are not constant, regular data capture will not only improve our understanding of the environmental changes but also aid better mitigate future changes. | |
| The study has provided an insight into the impact of land surface temperature on vegetation. When associated with climate change, the results could ultimately provide insights into the complex interactions between changes in temperature and vegetation cover. Knowing the effects of fluctuating land surface temperature can be used to identify areas of potential vulnerability and implementation of precautionary measures. | |
Acknowledgment |
|
| The authors would like to acknowledge Tauhara Middle 15 Trust who consented to this study being conducting on their land. The authors would like to thank Taupo District Council for supplying the aerial images and Contact Energy Ltd for supplying the thermal infrared imagery. Many thanks to Steve Richards, Ajaya Bharadwaja and Sarah Beadel for their time. | |
References |
|
|
|
