A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirement for the degree of Masters of Science (GIS and Remote Sensing) at the School of Geography, Archaeology & Environmental Studies / Tamarix spp, commonly known as tamarisk or salt cedar, belong to the family of Tamaricaceae. It is a phreaphytic halophyte with 55 species in the genus Tamarix. South Africa has one indigenous (Tamarix usneoides) and two exotic (T. ramosissima and T.chinensis). Not only are the exotic Tamarix species becoming infamous invaders, but their hybridisation with the indigenous T. usneoides is also complicating morphological discrimination between the different species, and the prospect of potential use of bio-control agents to curb invasion. Thus, lack of spatial information about the current and the past distribution of tamarisk have hampered the effort to control its invasion. This study aimed at investigating the use of multi-temporal remotely sensed data to map the exotic Tamarix invasion in the riparian ecosystem of the Western Cape Province of South Africa, where it predominantly occurs. Random Forest (RF) and Support Vector Machine (SVM) were tested to classify Tamarix and other land-cover types. Sentinel 2 data and Landsat OLI earth observation data were used to map the current and the temporal exotic Tamarix distribution between 2007 and 2018, respectively. This included mapping the current and the multi-temporal Tamarix extent of invasion using the multi-spectral sensors Sentinel 2 and Landsat 5 and 8, respectively. Sentinel 2 was able to detect and discriminate the exotic Tamarix spp invasion using RF and SVM algorithms. The Random Forest classification achieved an overall accuracy of 87.83% and kappa of 0.85, while SVM achieved an overall accuracy of 86.31% and kappa of 0.83. Multi-temporal Landsat data was able to map the current and previous extent of exotic Tamarix invasion for the period between 2007 and 2018. Six land-cover types were classified using SVM. The overall accuracies achieved for 2007, 2014 and 2018 were 87.66%, 91.10%, and 90.62% respectively, and the kappa were 0.85, 0.89, and 0.88, respectively. It was found that the exotic Tamarix invasion increased from 284.67 ha to 647.10 ha in De Rust area, 74.70 ha to 97.29 ha in Leeu Gamka and 215.01 ha to 544.41 ha in Prince Albert region in a period of 11 years. Sentinel 2 and Landsat data have shown the potential to be used in Tamarix mapping. The results obtained in this study would help in implementation of conservation and rehabilitation plans. / GR 2020

Studies have not been conducted examining the influence of the spatial distribution of LiDAR-derived tree measuresments and their affects the predictive ability of LiDAR-derived forest metrics as input for growth-and-yield analysis on individual trees. This study addresses both of these voids in current knowledge and determines the suitability, concerns and application of LiDAR for time-series analysis, specifically forest growth-and-yield. LiDAR datasets of the same site acquired in 1999, 2000, 2002, and 2006 by different vendors using different specifications were utilized in this study. Directional differences of Lidar-identified tree top locations were examined. Minimal location differences were noted, but no bias occurred. Differences in locations appeared to be from environmental effects such as wind. Improvements on individual-tree identification using a time-series analysis approach were implemented. The treeinding model was improved with a Boolean decision rule yielding significant differences in stand density calculations in 1.4 m spacing plots and for overall calculations of the 2000 and 2002 LiDAR datasets. Individual tree measurements derived from the 1999 LiDAR data were used to estimate growth to the 2006 data. These growth-and-yield values were compared with field-derived and field-measured values. Significant differences were found between the LiDAR- and field-derived measures of growth-and-yield. These increased over time and were believe to be compounded error from the LiDAR-estimated tree diameters. LiDAR datasets can be correlated to previous LiDAR datasets of the same area with very little effort. LiDAR tree identification can be improved using decision criteria based on subsequent LiDAR datasets of the same area. The ability to track individual trees by location over time using LiDAR could yield large datasets to potentially improve growth-and-yield modeling efforts and other stand characterization procedures.

Regional crop yield estimations using crop models is a national priority due to its contributions to crop security assessment and food pricing policies. Many of these crop yield assessments are performed using time-consuming, intensive field surveys. This research was initiated to test the applicability of remote sensing and grid-based meteorological model data for providing improved and efficient predictive capabilities for crop bio-productivity. The soybean prediction model (Sinclair model) used in this research, requires daily data inputs to simulate yield which are temperature, precipitation, solar radiation, day length initialization of certain soil moisture parameters for each model run. The traditional meteorological datasets were compared with simulated South American Land Data Assimilation System (SALDAS) meteorological datasets for Sinclair model runs and for initializing soil moisture inputs. Considering the fact that grid-based meteorological data has the resolution of 1/8th of a degree, the estimations demonstrated a reasonable accuracy level and showed promise for increase in efficiency for regional level yield predictions. The research tested daily composited Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (both AQUA and TERRA platform) and simulated Visible/Infrared Imager Radiometer Suite (VIIRS) sensor product (a new sensor planned to be launched in the near future) for crop growth and development based on phenological events. The AQUA and TERRA fusion based daily MODIS NDVI was utilized to developed a planting date estimation method. The results have shown that daily MODIS composited NDVI values have the capability for enhanced monitoring of the soybean crop growth and development with respect to soybean growth and development. The method was able to predict planting date within ±3.4 days. A geoprocessing framework for extracting data from the grid data sources was developed. Overall, this study was able to demonstrate the utility of MODIS and VIIRS NDVI datasets and SALDAS meteorological data for providing effective inputs to crop yield models and the ability to provide an effective remote sensing-based regional crop monitoring. The utilization of these datasets helps in eliminating the ground-based data collection, which improves cost and time efficiency and also provides capability for regional crop monitoring.

Multiple linear and ordinal logistic regression methods were used to develop cubic foot volume (outside bark to a pulpwood diameter top) estimation models for the central Mississippi Institute for Forest Inventory (MIFI) inventory region of Mississippi, USA based on multi-scene Landsat derived variables. These models were used to stratify the region into volume classes to estimate the statistical gains made from a stratified random sample versus a complete random sample. Ordinal logistic regression produced higher accuracy statistics for all forest cover classes except the mixed forest cover class and the method is recommended to be used to estimate cubic foot volume (outside bark to a pulpwood diameter top) for the study area. Statistical gains from ordinal logistic regression averaged 30.34% and relative precision averaged 1.53 for the study area. For each forest cover type volume model that was produced, it was found that the interaction variable between Landsat TM band 5 and the GIS age variable was statistically significant.

Agricultural production has observed many changes in technology over the last 20 years. Producers are able to utilize technologies such as site-specific applicators and remotely sensed data to assist with decision making for best management practices which can improve crop production and provide protection to the environment. It is known that plant stress can interfere with photosynthetic reactions within the plant and/or the physical structure of the plant. Common types of stress associated with agricultural crops include herbicide induced stress, nutrient stress, and drought stress from lack of water. Herbicide induced crop stress is not a new problem. However, with increased acreage being planting in varieties/hybrids that contain herbicide resistant traits, herbicide injury to non-target crops will continue to be problematic for producers. With rapid adoption of herbicide-tolerant cropping systems, it is likely that herbicide induced stress will continue to be a major concern. To date, commercially available herbicide-tolerant varieties/hybrids contain traits which allow herbicides like glyphosate and glufosinate-ammonium to be applied as a broadcast application during the growing season. Both glyphosate and glufosinate-ammonium are broad spectrum herbicides which have activity on a large number of plant species, including major crops like non-transgenic soybean, corn, and cotton. Therefore, it is possible for crop stress from herbicide applications to occur in neighboring fields that contain susceptible crop varieties/hybrids. Nutrient and moisture stress as well as stress caused by herbicide applications can interact to influence yields in agricultural fields. If remotely sensed data can be used to accurately identify specific levels of crop stress, it is possible that producers can use this information to better assist them in crop management to maximize yields and protect their investments. This research was conducted to evaluate classification of specific crop stresses utilizing hyperspectral remote sensing.

This project developed a series of spatial models to classify the island karst landforms and predict the island karst feature distribution. Spatial models with unsupervised classified images, and fuzzy-based spatial models were used in this study. Forecasting verification and spatial regressions were used to validate the models. The case study was conducted on San Salvador Island, the Bahamas, a recognized carbonate island with island karst features. Fieldwork data on banana holes on the island were used for model validation. The results showed that most models had accuracy higher than 90%, and were statistically proved that they could be used as predictors of island karst features. Further study may be conducted to solve the Modified Areal Unit Problem (MAUP) in the future.

Cyanobacterial algal bloom is a major water quality issue in inland lakes, reservoirs, and estuarine environments because of its scum and bad odor forming and toxin producing abilities. Health risks from cyanobacterial toxin can vary from skin irritations to fever, intestinal problems, and neurological disorders. Terminations of blooms also cause oxygen depletion leading to hypoxia and widespread fish kills. Adding to the problem, many species of cyanobacteria produce odorous compounds such as geosmin and 2-methylisoborneol (MIB) that cause “earthy-muddy” and “musty” odor in drinking water, which is also a serious issue in aquaculture and drinking water industry. Therefore continuous monitoring of cyanobacterial presence in recreational water bodies, surface drinking water sources, and water bodies dedicated for aquaculture is highly required for their early detection and subsequent issuance of a health warning and reducing the economic loss. Remote sensing techniques offers the capability of identifying and monitoring cyanobacterial blooms in a synoptic scale. Over the years, the scientific community has focused on developing methods to quantify cyanobacterial biomass using phycocyanin,an accessory photosynthetic pigment, as a marker pigment. However, because of the confounding influence of chlorophyll-a and other photo pigments, remote retrieval of phycocyanin signal from turbid productive water has been a difficult task. This dissertation analyzes the potential of remote sensing techniques and develops empirical and quasi-analytical algorithms to isolate the phycocyanin signal from the remote sensing reflectance data using a set of radiative transfer equations and retrieves phycocyanin concentration in the water bodies. An extensive dataset, consisting of in situ radiometric measurements, absorption measurements of phytoplankton, colored dissolved organic matter, detritus, and pigment concentration, was used to optimize the algorithms. Validations of all algorithms were also performed using an independent dataset and errors and uncertainties from the algorithms were discussed. Despite the simplicity, an empirical model produced highest accuracy of phycocyanin retrieval, whereas, the newly developed quasi-analytical phycocyanin algorithm performed better than the existing semi-analytical algorithm. Results show that remote sensing techniques can be used to quantify cyanobacterial phycocyanin abundance in turbid and hypereutrophic waters.

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