South Africa’s growing elephant populations are concentrated in relatively small enclosed protected areas resulting in the over utilisation of the available food sources. Elephants and other herbivores as well as other natural disturbances such as fires and droughts play an important role in maintaining savannah environments. When these disturbances become too concentrated in a particular area the vegetation composition may be negatively affected. Excessive damage to the vegetation would result from exceeding the capacity of a protected area to provide food resources. The effect of the 120 elephants on the vegetation of Welgevonden Private Game Reserve, is not known. The rugged terrain of this reserve makes it a difficult, time consuming and labour intensive exercise to conduct ground studies. Satellite images can be used as a monitoring tool for vegetation change and improve the quantity and quality of environmental data to be collected significantly, allowing more informed management decision-making. This study evaluated the use of satellite imagery for monitoring elephant induced vegetation change on Welgevonden Private Game Reserve. The LANDSAT Thematic Mapper multispectral images, acquired at two yearly intervals from 1993 until 2007 were used. However, no suitable images were available for the years 1997, 2001 and 2003. A series of vegetation change maps was produced and the distribution of water sources and fire occurrences mapped. The areas of change were then correlated with the spatial distribution of water points and fire occurances, with uncorrelated areas of change. This was analysed using large animal population trends, weather data and management practices. On the visual comparison of the vegetation maps, it was seen that over this time period there was some decrease and thinning of woodland, but the most notable change was the increase of open woodland and decrease in grasslands. Using only the digital change detection for the period 1993 to 2007, a general increase in vegetation cover is seen. But this generalisation is misleading, since comparing the digital change detection to the vegetation maps indicates that while vegetation cover may have increased, significant changes occurred in the vegetation types. Most of the areas of significant change that were identified showed a strong positive correlation with burnt areas. The distribution of the water sources could not be directly linked to the vegetation change although rainfall fluctuations seemed to have accelerated vegetation changes. Years with high game counts, such as 1999, also coincide with very low rainfall making it difficult to differentiate between the effects of heavy utilisation of vegetation and low rainfall. Furthermore, many of the initial vegetation changes could be the result of land use changes due to the introduction of browsers, selective grazers and elephants that allow for more natural utilisation of the vegetation. Remote sensing makes it possible to successfully track changes in vegetation and identify areas of potential elephant induced vegetation change. Vegetation changes caused by disturbances, such as fire and anthropogenic activities, can be accounted for but it is not possible to conclude with a high level of certainty that the further changes seen are solely a result of elephant damage. Further work is required to reliably isolate elephant induced vegetation changes, as well as to establish the effects these changes have on the ecosystem as a whole. / Environmental Sciences / (M. Sc. (Environmetal Sciences))

Vegetation status is a key indicator of the ecosystem condition in a particular area. The study objective was about the estimation of leaf nitrogen (N) as an indicator of vegetation water stress using vegetation indices especially the red edge based ones, and how leaf N concentration is influenced by various environmental factors. Leaf nitrogen was estimated using univariate and multivariate regression techniques of stepwise multiple linear regression (SMLR) and random forest. The effects of environmental parameters on leaf nitrogen distribution were tested through univariate regression and analysis of variance (ANOVA). Vegetation indices were evaluated derived from the analytical spectral device (ASD) data, resampled to RapidEye. The multivariate models were also developed to predict leaf N. The best model was chosen based on the lowest root mean square error (RMSE) and higher coefficient of determination (R2) values. Univariate results showed that red edge based vegetation index called MERRIS Terrestrial Chlorophyll Index (MTCI) yielded higher leaf N estimation accuracy as compared to other vegetation indices. Simple ratio (SR) based on the bands red and near-infrared was found to be the best vegetation index for leaf N estimation with exclusion of red edge band for stepwise multiple linear regression (SMLR) method. Simple ratio (SR3) was the best vegetation index when red edge was included for stepwise linear regression (SMLR) method. Random forest prediction model achieved the highest leaf N estimation accuracy, the best vegetation index was Red Green Index (RGI1) based on all bands with red green index when including the red edge band. When red edge band was excluded the best vegetation index for random forest was Difference Vegetation Index (DVI1). The results for univariate and multivariate results indicated that the inclusion of the red edge band provides opportunity to accurately estimate leaf N. Analysis of variance results showed that vegetation and soil types have a significant effect on leaf N distribution with p-values<0.05. Red edge based indices provides opportunity to assess vegetation health using remote sensing techniques. / Environmental Sciences / M. Sc. (Environmental Management)

Knowledge of the geospatial distribution of vegetation is fundamental for resource management. The objective of this study is to investigate the possible use of airborne LIDAR (light detection and ranging) data to improve classification accuracy of high spatial resolution optical imagery and compare the ability of two classification algorithms to accurately identify and map wetland vegetation communities. In this study, high resolution imagery integrated with LIDAR data was compared jointly and alone; and the nearest neighbor (NN) and machine learning random forest (RF) classifiers were assessed in semi-automated geographic object-based image analysis (GEOBIA) approaches for classification accuracy of heterogeneous vegetation assemblages at Everglades National Park, FL, USA. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

South Africa’s growing elephant populations are concentrated in relatively small enclosed protected areas resulting in the over utilisation of the available food sources. Elephants and other herbivores as well as other natural disturbances such as fires and droughts play an important role in maintaining savannah environments. When these disturbances become too concentrated in a particular area the vegetation composition may be negatively affected. Excessive damage to the vegetation would result from exceeding the capacity of a protected area to provide food resources. The effect of the 120 elephants on the vegetation of Welgevonden Private Game Reserve, is not known. The rugged terrain of this reserve makes it a difficult, time consuming and labour intensive exercise to conduct ground studies. Satellite images can be used as a monitoring tool for vegetation change and improve the quantity and quality of environmental data to be collected significantly, allowing more informed management decision-making. This study evaluated the use of satellite imagery for monitoring elephant induced vegetation change on Welgevonden Private Game Reserve. The LANDSAT Thematic Mapper multispectral images, acquired at two yearly intervals from 1993 until 2007 were used. However, no suitable images were available for the years 1997, 2001 and 2003. A series of vegetation change maps was produced and the distribution of water sources and fire occurrences mapped. The areas of change were then correlated with the spatial distribution of water points and fire occurances, with uncorrelated areas of change. This was analysed using large animal population trends, weather data and management practices. On the visual comparison of the vegetation maps, it was seen that over this time period there was some decrease and thinning of woodland, but the most notable change was the increase of open woodland and decrease in grasslands. Using only the digital change detection for the period 1993 to 2007, a general increase in vegetation cover is seen. But this generalisation is misleading, since comparing the digital change detection to the vegetation maps indicates that while vegetation cover may have increased, significant changes occurred in the vegetation types. Most of the areas of significant change that were identified showed a strong positive correlation with burnt areas. The distribution of the water sources could not be directly linked to the vegetation change although rainfall fluctuations seemed to have accelerated vegetation changes. Years with high game counts, such as 1999, also coincide with very low rainfall making it difficult to differentiate between the effects of heavy utilisation of vegetation and low rainfall. Furthermore, many of the initial vegetation changes could be the result of land use changes due to the introduction of browsers, selective grazers and elephants that allow for more natural utilisation of the vegetation. Remote sensing makes it possible to successfully track changes in vegetation and identify areas of potential elephant induced vegetation change. Vegetation changes caused by disturbances, such as fire and anthropogenic activities, can be accounted for but it is not possible to conclude with a high level of certainty that the further changes seen are solely a result of elephant damage. Further work is required to reliably isolate elephant induced vegetation changes, as well as to establish the effects these changes have on the ecosystem as a whole. / Environmental Sciences / (M. Sc. (Environmetal Sciences))

Vegetation status is a key indicator of the ecosystem condition in a particular area. The study objective was about the estimation of leaf nitrogen (N) as an indicator of vegetation water stress using vegetation indices especially the red edge based ones, and how leaf N concentration is influenced by various environmental factors. Leaf nitrogen was estimated using univariate and multivariate regression techniques of stepwise multiple linear regression (SMLR) and random forest. The effects of environmental parameters on leaf nitrogen distribution were tested through univariate regression and analysis of variance (ANOVA). Vegetation indices were evaluated derived from the analytical spectral device (ASD) data, resampled to RapidEye. The multivariate models were also developed to predict leaf N. The best model was chosen based on the lowest root mean square error (RMSE) and higher coefficient of determination (R2) values. Univariate results showed that red edge based vegetation index called MERRIS Terrestrial Chlorophyll Index (MTCI) yielded higher leaf N estimation accuracy as compared to other vegetation indices. Simple ratio (SR) based on the bands red and near-infrared was found to be the best vegetation index for leaf N estimation with exclusion of red edge band for stepwise multiple linear regression (SMLR) method. Simple ratio (SR3) was the best vegetation index when red edge was included for stepwise linear regression (SMLR) method. Random forest prediction model achieved the highest leaf N estimation accuracy, the best vegetation index was Red Green Index (RGI1) based on all bands with red green index when including the red edge band. When red edge band was excluded the best vegetation index for random forest was Difference Vegetation Index (DVI1). The results for univariate and multivariate results indicated that the inclusion of the red edge band provides opportunity to accurately estimate leaf N. Analysis of variance results showed that vegetation and soil types have a significant effect on leaf N distribution with p-values<0.05. Red edge based indices provides opportunity to assess vegetation health using remote sensing techniques. / Environmental Sciences / M. Sc. (Environmental Management)

The vegetation of the Nooitgedacht section of the Loskop Dam Nature Reserve resembles Bankenveld vegetation and differs from the other areas of the reserve. This study was undertaken to identify, classify, and describe the plant communities present on this section, and to determine their veld condition. The Braun-Blanquet approach was followed to classify the different plant communities. A total number of 170 sample plots (100m2) were placed in all homogeneous vegetation units in a randomly stratified basis. The Ecological Index Method (EIM) was used to determine the veld condition. Data were collected using the steppoint method and incorporated into the GRAZE model from where the veld condition was calculated. A minimum of 400 step points were surveyed in each community with more points in the larger communities. Plant community data was analysed using the JUICE software program. A total of 11 plant communities were identified. The overall veld condition score indicates the vegetation to be in a good condition, resulting in a high grazing capacity. / Environmental Sciences / M. Sc. (Nature Conservation)

The vegetation of the Nooitgedacht section of the Loskop Dam Nature Reserve resembles Bankenveld vegetation and differs from the other areas of the reserve. This study was undertaken to identify, classify, and describe the plant communities present on this section, and to determine their veld condition. The Braun-Blanquet approach was followed to classify the different plant communities. A total number of 170 sample plots (100m2) were placed in all homogeneous vegetation units in a randomly stratified basis. The Ecological Index Method (EIM) was used to determine the veld condition. Data were collected using the steppoint method and incorporated into the GRAZE model from where the veld condition was calculated. A minimum of 400 step points were surveyed in each community with more points in the larger communities. Plant community data was analysed using the JUICE software program. A total of 11 plant communities were identified. The overall veld condition score indicates the vegetation to be in a good condition, resulting in a high grazing capacity. / Environmental Sciences / M. Sc. (Nature Conservation)