Global climate change is expected to be accompanied by changes in the composition of plant functional types. Such changes are predicted to follow shifts in the percentage cover and abundance of grass species, following the C3 and C4 photosynthetic pathways. These two groups differ in a number of physiological, structural and biochemical aspects. It is important to measure these characteristic properties because they affect ecosystem processes, such as nutrient cycling. High spectral and spatial resolution remote sensing systems have been proven to offer data, which can be used to accurately detect, classify and map plant species. The major challenge, however, is that the spectral reflectance data obtained over many narrow contiguous channels (i.e. hyperspectral data) represent multiple classes that are often mixed for a limited training-sample size. This is commonly referred to as the Hughes phenomenon or “the curse of dimensionality”. In the context of hyperspectral data analysis, the Hughes phenomenon often introduces a high degree of multicollinearity, which is caused by the use of highly-correlated spectral predictors. Multicollinearity is a prominent problem in processing hyperspectral data for vegetation applications, due to similarities in the spectral reflectance properties of biophysical and biochemical attributes. This study explored an innovative method to solve the problems associated with spectral dimensionality and the related multicollinearity, by developing a user-defined inter-band correlation filter function to resample hyperspectral data. The proposed resampling technique convolves the spectral dependence information between a chosen band-centre and its shorter and longer wavelength neighbours. The utility of the new resampling technique was assessed for discriminating C3 (Festuca costata) and C4 (Themeda triandra and Rendlia altera) grasses and for predicting their nutrient content (nitrogen, protein, moisture, and fibre), using partial least squares and random forest regressions. In general, results obtained showed that the user-defined inter-band correlation filter technique can mitigate the problem of multicollinearity in both classification and regression analyses. Wavebands in the shortwave infrared region were found to be very important in regression and classification analyses, using field spectra-only datasets. Next, the analyses were up-scaled from field spectra to the new generation multispectral satellite, WorldView-2 imagery, which was acquired for the Cathedral Peak region of the Drakensberg Mountains. The results obtained, showed that the WV2 image data contain useful information for classifying the C3 and C4 grasses and for predicting variability in their nitrogen and fibre concentrations. This study makes a contribution by developing a user-defined inter-band correlation filter to resample hyperspectral data, and thereby mitigating the high dimensionality and multicollinearity problems, in remote sensing applications involving C3 and C4 grass species or communities. / Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2013.

The Eastern Shores of Lake St Lucia forms part of the ecologically important Greater St Lucia Wetland Park, designated a World Heritage Site in 1999. The landscape is characterised by surface water, a high water table and numerous wetlands. Little is known about the distribution and temporal fluctuations of this surface water and its relationship to the wetlands. This study uses remote sensing to examine the relationship by mapping the extent of seasonal, ephemeral and permanent surface water on the Eastern Shores. Much of the surface water occurs in conjunction with emergent vegetation and is not easily mapped using hard classification methods. Neither a cluster analysis nor a maximum likelihood classification were able to map the subtle variations of the water-vegetation mix. Much more successful was the application of spectral mixture analysis using image endmembers of water, woody vegetation and non-woody vegetation. This technique was applied to seven Landsat Thematic Mapper images from 1991, 2001 and 2002. Steep slopes, forests and bare sand were masked out prior to classification. Maps of water extent were produced for each of the seven study dates. Mapping accuracy was verified against rainfall, with high correlations being obtained against rainfall accumulated over six months and longer. Long-term rainfall patterns were reflected in the surface water distribution, with inundation being more extensive when accumulated rainfall was high. Fire scars reduced the accuracy of the spectral mixture analysis but these scars could be identified from the thermal image bands. The largest open water body in the study area was Lake Bhangazi. Large extents of surface water were also found in the Mfabeni swamp and the wilderness area to the north where water concentrations of 90% were measured during wet periods. Surface water present near Brodies Crossing during wet periods was less evident when rainfall was lower. No inundation was recorded in the areas to the west and south-west of the Mfabeni swamp or in the southern parts of the study area. The techniques used in this study were developed into a water mapping protocol that uses image endmembers and spectral mixture analysis to measure water concentration. / Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2006.

Globally, water turbidity remains a crucial parameter in determining water quality. South Africa is largely regarded as arid and is often characterised by limited but high intensity rainfall. This characteristic renders most of the country’s water bodies turbid. Consequently, the use of turbidity as a measure of water quality is of great relevance in a South African context. Generally, turbidity alters biological and ecological characteristics of water bodies by inducing changes in among others temperature, oxygen levels and light penetration. These changes may affect aquatic life, ecosystem functioning and available water for industrial and domestic use. Siltation, a direct function of turbidity also impacts on the physical storage of dams and shortens their useful life. To date, determination of water turbidity relies on the tradition laboratory based methods that are often time consuming, expensive and labour intensive. This has increased the need for more cost effective means of determining water turbidity. In the recent past, the use of remote sensing techniques has emerged as a viable option in water quality assessment. Hyperspectral remote sensing characterizes numerous contiguous narrow bands that have great potential in water turbidity measurement. This study explored the applicability of hyperspectral data in water turbidity detection. It explored the visible and near-infrared region to select the optimal bands and indices for turbidity measurement. Using the Analytical Spectral Device (ASD) field spectroradiometer and a 2100Q portable turbidimeter, spectral reflectance and laboratory based turbidity measurements were taken from prepared turbid solutions of predetermined concentrations (i.e. 10g/l to 150g/l), respectively. The Pearson’s coefficient of correlation and R2 values were employed to select optimal spectral bands and indices. The findings showed a positive linear relationship between reflectance, the amount of soil in water and turbidity values. The strongest relationships came from bands 528, 489, 657, 1000 and 983, reporting adjusted R2 values of 0.7062, 0.7004, 0.6864, 0.7120 and 0.6961, respectively. The highest coefficient came from band 1000nm. The strongest indices were 625/440 and (770-1000)/(770+1000), with adjusted R2 values of 0.6822 and 0.6973 respectively. The use of hyperspectral data in turbidity detection is ideal for optimal band interrogation. Although good results were generated from this study, further investigations are needed in the near-infrared region. / Thesis (M.Env.Dev.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

The aim of this study was to evaluate potential applications of remote sensing technology in sugarcane agriculture, using the Umfolozi Mill Supply Area as a case study. Several objectives included the evaluation of remotely sensed satellite information for the following applications: mapping of sugarcane areas, identifying sugarcane characteristics including phenology, cultivar and yield, monitoring the sugarcane inventory throughout the milling season and yield prediction. Four Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) images were obtained for the 2001-2002 season. Mapping of sugarcane areas was conducted by .means of unsupervised hierarchical classifications, on three relatively cloud free, Tasseled Cap transformed images. The Brightness, Greenness and Wetness bands for each Tasseled Cap transformed image were combined into a single image for this classification. The investigation into relationships between satellite spectral reflectances and phenology, cultivar and yield involved the cosine of the solar zenith angle (COST) method for atmospheric correction of all four Landsat 7 ETM+ images. Detailed agronomic records and field boundary information, for a selection of sugarcane fields, were used to extract the at-satellite reflectances on a pixel basis . These values were stored in a relational database for analysis. Monitoring of the sugarcane inventory throughout the milling season was conducted by means of unsupervised classifications on the Brightness, Greenness and Wetness bands for each of the four time-step Tasseled Cap transformed images. Accurate field boundary information for all sugarcane fields was used to mask out non-sugarcane areas. The remaining sugarcane areas in each time-step image were then classified by means of unsupervised classification techniques to ascertain the relative proportions of the different land covers, namely: harvested immature and mature sugarcane by visual interpretation of the classification results. The yield forecasting approach utilized a time-step approach in which Vegetation Indices (VIs) were accumulated over different periods or time frames and compared with annual production. VIs were derived from both the National Oceanic and Atmospheric Administration (NOAA) and Landsat 7 ETM+ sensors. Different periods or times were used for each sensor. The results for the mapping of sugarcane areas showed that the mapping accuracies for the large scale grower fields was higher than for the small-scale growers. In both instances, the level of accuracy was below that of the recommended sugar industry mapping standard, namely 1% of the true area. Despite the low mapping accuracies, much benefit could be realized from the map product in terms of identifying new areas of sugarcane expansion. These would require detailed accurate mapping. The results for monitoring of the sugarcane inventory throughout showed that remote sensing, in conjunction with detailed field information, was able to accurately measure the areas harvested in each time-step image. These results may have highly beneficial applications in sugarcane supply management and monitoring. The results for time-step approach to yield forecasting yielded poor results in general. The Landsat derived VIs showed limited potential; however, the data were only available for one season, making it difficult to quantify the impact of climatic conditions on these results. All results for the time-step approach using NOAA data yielded negative results. The results for the investigation into relationships between satellite spectral reflectances and phenology, cultivar and yield showed that that different phenological stages of sugarcane growth were identifiable from Landsat 7 ETM+ at-satellite reflectances. The sugarcane yields and cultivar types were not correlated with the at-satellite reflectances. These results combined with the sugarcane area monitoring may provide valuable information in the management and monitoring of sugarcane supply. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2004.

No description available.

Land use/land cover (LULC) information is essential for a plethora of applications including environmental monitoring and natural resource management. Traditionally, field surveying techniques were the sole source of acquiring such information; however, these methods are labour intensive, costly and time consuming. With the advent of remote sensing, LULC information can be acquired in an economical, less tedious and non-time consuming manner at shorter temporal cycles and over larger areas. The aim of this study was to assess the utility of multispectral remote sensing data and the Random Forest (RF) algorithm to improve accuracy of LULC maps in heterogeneous ecosystems. The first part of this study used moderate resolution SPOT-5 data to compare the performance of the RF algorithm to that of the commonly used Maximum Likelihood (ML) classifier. Results indicated that RF performed significantly better than ML (66.1%) and yielded an overall accuracy of 80.2%. Moreover, RF variable importance measures were able to provide an insight on the bands that played a pivotal role in the classification process. Due to the fact that moderate resolution satellite data was used, both classifiers seemed to experience some difficulties in discriminating amongst classes that exhibited similar spectral responses such as Eucalyptus grandis and Pinus tree plantations, young sugarcane and mature sugarcane, as well as river and ocean water. In that regard, the next section attempted to address this shortfall. The second part of the study used high resolution multispectral data acquired from the WorldView-2 sensor to discriminate amongst six spectrally similar LULC classes using the advanced RF algorithm. Results suggested that the use of WorldView-2 data together with the RF ensemble algorithm is a robust and accurate method for separating classes exhibiting similar spectral responses. The classification process yielded an overall accuracy of 91.23% and also provided valuable insight into WorldView-2 bands that were most suitable for discriminating the LULC categories. Overall, the study concluded that: (i) multispectral remote sensing data is an effective tool for obtaining accurate and timely LULC information, (ii) moderate resolution multispectral data can be used to map broad LULC categories whereas high resolution multispectral data can be used to separate LULC at finer levels of detail, (iii) RF is a robust and effective tool for producing LULC maps that are less prone to error. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

An understanding of the distribution of the Bracken fern (Pteridium aquilinum (L.) Kuhn) is critical for providing an appropriate management strategy. In this regard, remote sensing can play a critical role in mapping and modelling such distribution. In this study, an integrated approach using the random forest, maximum likelihood and vegetation indices was developed and tested to determine the capability of WorldView-2 multispectral eight band image in characterising the Bracken fern. Results based on the WorldView-2 were further compared to SPOT-5 multispectral (MS) image findings. The WorldView-2 (WV-2) image was spectrally resized to four traditional bands (blue, 450-510nm; green, 510-580 nm; red, 630-690 nm and NIR1, 770-895 nm) and four additional bands (coastal blue, 400-450 nm; yellow, 585-625 nm; red-edge, 705-745 nm and NIR2, 860-1040 nm) to evaluate the practicality of the spectral resolution in mapping the Bracken fern. The results from this analysis showed that the spectrally resized additional bands were more successful in general land cover mapping and characterising the Bracken fern. The result’s overall accuracy was 79.14% while the user’s and producer’s accuracies were 97.62% and 91.11% respectively. The second part of the study sought to improve the classification accuracy by applying a robust machine learning algorithm, the random forest. Since the random forest does not automatically choose the optimal bands, the backward variable elimination technique was employed to identify the optimum wavelengths in WV-2 for the identification of the Bracken fern. Respective out-of-bag (OOB) errors of 13.1% and 9.17% were achieved when the WV-2’s eight bands and optimally selected bands (n= 5) were used. These bands lie in the green (510-580nm), near-infrared1 (770-895nm), red-edge (705-745nm), near-infrared2 (860-1040nm) and the coastal blue (400-450nm) regions of the electromagnetic spectrum. These findings confirm the importance of the additional bands in vegetation analyses. The vegetation indices computed from these regions of the spectrum were superior to those in the visible region. The classification accuracy using WV-2 bands was superior to that from the commonly used SPOT 5 image. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2014.

Assessment of leaf and canopy chlorophyll content provides information on plant physiological status; it is related to nitrogen content and hence, photosynthesis process, net primary productivity and carbon budget. In this study, a method is developed for the retrieval of total chlorophyll content (Chlorophyll a+b) per unit leaf and per unit ground area based on improved vegetation structural parameters which are derived using multispectral multi-angle remote sensing data. Structural characteristics such as clumping and gaps within a canopy affect its solar radiation absorption and distribution and impact its reflected radiance acquired by a sensor. One of the main challenges for the remote sensing community is to accurately estimate vegetation structural parameters, which inevitably influence the retrieval of leaf chlorophyll content. Multi-angle optical measurements provide a means to characterize the anisotropy of surface reflectance, which has been shown to contain information on vegetation structural characteristics. Hyperspectral optical measurements, on the other hand, provide a fine spectral resolution at the red-edge, a narrow spectral range between the red and near infra-red spectra, which is particularly useful for retrieving chlorophyll content. This study explores a new refined measurement concept of combining multi-angle and hyperspectral remote sensing that employs hyperspectral signals only in the vertical (nadir) direction and multispectral measurements in two additional (off-nadir) directions within two spectral bands, red and near infra-red (NIR). The refinement has been proposed in order to reduce the redundancy of hyperspectral data at more than one angle and to better retrieve the three-dimensional vegetation structural information by choosing the two most useful angles of measurements. To illustrate that hyperspectral data acquired at multiple angles exhibit redundancy, a radiative transfer model was used to generate off-nadir hyperspectral reflectances. It has been successfully demonstrated that the off-nadir hyperspectral simulations could be closely reconstructed based on the nadir hyperspectral reflectance and off-nadir multi-spectral reflectance in the red and NIR bands. This is shown using the Compact High-resolution Imaging Spectrometer (CHRIS) and Compact Airborne Spectrographic Imager (CASI) data acquired over a forested area in the Sudbury region (Ontario, Canada). Through intensive validation using field data, it is demonstrated that the combination of reflectances at two angles, the hotspot and darkspot, through the Normalized Difference between Hotspot and Darkspot (NDHD) index has the strongest response to changes in vegetation clumping, an important structural component of canopy. Clumping index (Ω) and Leaf Area Index (LAI) maps are generated based on previous algorithms as well as empirical relationships developed in this study. To retrieve chlorophyll content, inversion of the 5-Scale model is performed by developing Look-Up Tables (LUTs) that are based on the improved structural characteristics developed using multi-angle data. The generated clumping index and LAI maps are used in the LUTs to estimate leaf reflectance. Inversion of the leaf reflectance model, PROSPECT, is further employed to estimate chlorophyll content per unit leaf area. The estimated leaf chlorophyll contents are in good agreement with field-measured values. The refined measurement concept of combining hyperspectral with multispectral multi-angle data provides the opportunity for simultaneous retrieval of vegetation structural and biochemical parameters.

This study investigated the possibility of three approaches in determination of soil moisture in the root zone. The aim of the study was to contribute to the development of soil moisture monitoring methods to better help crop best management practices. / Two fields were examined, one at the Macdonald Campus of McGill University and the other near St. Jean-sur-Richelieau. Three approaches were used; (1) a hand-held hyper-spectral sensor (350-2500 nm), (2) a Geonics RTM EM-38 conductivity meter and, (3) gravimetric soil moisture sampling. / The first experiment (at St. Jean-sur-Richelieu) investigated the possibility of monitoring soil moisture with the EM_38, in the presence of field elevation and soil texture. The second experiment (at Macdonald Campus) investigated the possibility of using hyper-spectral sensor data for determination of soil characteristics in the root zone, in the presence of such factors as (a) irrigation (main treatment), (b) nitrogen (sub-treatment), and (c) weed control (sub-sub-treatment). Statistical regression analyses and Artificial Neural Network models were used to select the best waveband region for determination of soil root zone moisture. / The coefficients of determination obtained by the statistical analyses ranged from 0.75 to 0.94. The wavebands most frequently identified by these analyses ranged from 1100 nm-1900 nm. / The performances of the ANN training models were considered acceptable (R2 from 0.6 to 0.8). The lack of sufficient data greatly impacts this approach.

Thesis (Ph. D.)--University of Hawaii at Manoa, 1994. / Includes bibliographical references (leaves 222-247). / Microfiche. / xvii, 246 p. ill. (some col.), maps 29 cm