No description available.

Support for sustainable agriculture by farmers and consumers is increasing as environmental and socio-economic issues rise due to more intensive farm practices. Site-specific crop management is an important component of sutainable agriculture, within which remote sensing can play an integral role. Field and image data were acquired over a farm in Saskatchewan as part of a national research project to demonstrate the advantages of site-specific agriculture for farmers. This research involved the estimation of crop biophysical parameters from airborne hyperspectral imagery using Spectral Mixture Analysis (SMA), a relatively new sub-pixel scale image processing method that derives the fraction of sunlit canopy, soil and shadow that is contributing to a pixel's relectance. SMA of three crop types (peas, wheat and canola) performed slightly better than conventional vegetation indices in predicting leaf area index (LAI) and biomass using Probe-1 imagery acquired early in the growing season. Other potential advantages for SMA were also indentified, and it was conclude that future research is warranted to assess the full potential of SMA in a multi-temporal sense throughout the growing season. / xiv, 194 leaves : ill. (some col.) ; 29 cm.

Cannabis sativa L. is a drug producing crop that is illegally cultivated in South Africa. The South African Police Service (SAPS) use aerial spotters on low flying fixed wing aircrafts to identify cannabis from other land cover. Cannabis is usually intercropped with maize to conceal it from law enforcement officers. Therefore the use of remote sensing in identifying and monitoring cannabis when intercropped with maize and other crops is imperative. This study aimed to investigate the potential of hyper spectral indices to discriminate cannabis from maize under different cropping methods, namely, monocropped and intercropped. Cannabis and maize were grown in a greenhouse. The spectral signatures were measured in a dark room environment. Green pigments (chlorophyll and carotenoid) from the treatments were also measured. These pigments were then compared with their respective indices. Photosynthetic reflective index (PRI) and Carotenoid Reflective Index (CRI) were two of the indices used to discriminate cannabis from maize using carotenoid content while the Red Edge Position (REP) and the narrow band Normalized Difference Vegetation Index (NDVI) used chlorophyll content and morphological differences respectively to discriminate the two plant species. CRI and NDVI proved to be capable of identifying cannabis under the two cropping conditions. NDVI showed a 25% spectral over lap for the monocropped treatments and 60% over lap for the intercropped treatments. CRI displayed 18% and 58% over lap for the monocropped and intercropped treatments, respectively. As a result CRI emerged as the most suitable index for discriminating cannabis from maize. With proper calibration of airborne or space borne imagery, the study offers potential to detect cannabis using remote sensing technology. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.

Mopane (Colophospermum mopane) woodlands are a source of valuable resources that contribute substantially to rural economies and nutrition across Southern Africa. However, a number of factors such as over-harvesting and climate change have brought the sustainability of the mopane woodland resources into question. Insect defoliation remains a major factor contributing to the depletion of woodland resources in rural areas resulting in low vitality and productivity of the woodland. Conventional methods (e.g. visual evaluation) have been used in monitoring insect defoliated areas in the past. These methods are costly and timeconsuming, because of the need to collect data immediately before and after an extreme event. In this regard, remote sensing techniques offer a practical and economical means of quantifying woodland degradation over large areas. Remote sensing is capable of providing rapid, relatively inexpensive, and near-real-time data that could be used for monitoring insect defoliation especially in semi-arid areas where data collection may be difficult. The present study advocates the development of techniques based on remotely sensed data to detect and map defoliation levels in Mopane woodland. The first part of the study provides an overview of remote sensing of insect defoliation, the implications for detecting and mapping defoliation levels as well as the challenges and need for further research especially within Mopane woodland. Secondly, the study explored whether Mopane species can be discriminated from each of its co-existing species using remote sensing. This was done as a prerequisite for classifying defoliation on mopane trees. Results showed that, with limited training samples, especially in semi-arid areas, Mopane trees can be reliably discriminated from its co-existing species using machine learning algorithms and multispectral sensors with strategic bands located in sensors such as RapidEye. These positive results prompted the need to test the use of ground based hyperspectral data and machine learning algorithm in identifying key spectral bands to discriminate different levels of insect defoliation. Results showed that the random forest algorithm (RF) simplified the process and provided the best overall accuracies by identifying eight spectral wavelengths, seven of which belongs to the red-edge region of electromagnetic spectrum. Furthermore, we tested the importance of the red-edge region of a relatively cheaper RapidEye imagery in discriminating the different levels of insect defoliation. Results showed that the red-edge region played an important role in mapping defoliation levels within Mopane woodland with NDVI-RE performing better than the traditional NDVI. Thirdly, the study tested the reliability and strength of the internal validation technique of RF in classifying different defoliation levels. It was observed that the bootstrapping internal estimate of accuracy in RF was able to provide relatively lower error rates (0.2319) for classifying a small dataset as compared to other validation techniques used in this study. Moreover, it was observed that the errors produced by the internal validation methods of RF algorithm was relatively stable based on the confidence intervals obtained compared to other validation techniques. Finally, in order to evaluate the effects of insect defoliation on the biophysical properties of mopane canopies at different defoliation levels, the study estimated leaf area index (LAI) of different defoliation levels based on simulated data. This was done using PROSAILH radiative transfer model inverted with canopy spectral reflectance extracted from RapidEyeRapidEye imagery by means of a look-up-table (LUT). It was observed that the significant differences exist between the defoliation levels signifying reduction in the LAI as a result of the defoliation. Furthermore, results showed that the estimated LAI was in the range of those reported in literature. The NDVI-RE index was the most strongly correlated with the estimated LAI as compared to other variables (RapidEye bands and NDVI). Overall, the study demonstrated the potential of remote sensing techniques in discriminating the state of Mopane woodland after insect defoliation. The results are important for establishing an integrated strategy for managing defoliation processes within Mopane veldt, thereby satisfying both the needs of local populations for Mopane trees and the worms. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

A research report submitted In partial fulfilment for the degree Master of Science (Geographical Information Systems & Remote Sensing). to the School of Geography, Archaeology & Environmental Studies, University of the Witwatersrand, Johannesburg , July 2017 / The nature of urban development experienced by mining towns across the world has been a subject of concern among urban planners because of its transitory nature. Most times mining towns develop gloriously into booming urban centres that create employment, generate wealth and satisfaction. All these fades into oblivion as soon as the mines get depleted. Mining towns often go through a number of urban processes which have been considered an expression of ‘infrastructural violence’ especially in the earlier stage of urban growth, and continually persists throughout the town’s life span. This research sought to reconstruct the history of urban development in the mining town of Virginia, Free State, and to quantify the manifestations of infrastructural violence throughout its timeline using GIS and remote sensing. Hence, land use and land cover maps were produced from aerial photographs, topographical maps and Landsat images through manual on-screen digitizing and classification using supervised support vector machine algorithms. Land use change detection analysis was conducted on the produced images using the cross classification and tabulation tool of QGIS 2.18.4 and the post classification tool of ENVI 5.3. Landscape metrics were employed to calculate the dimensions of growth and change experienced by all the land use classes during the timeline under study. Results obtained from this study confirmed the thoughts and findings of several theories vis a vis the nature of mining towns. Results reveal a rapid growth in the urban formal land use class up until 1995 with urban expansion and sprawl happening in the years between 1986 and 1995 with metrics of CA, NP and ED multiplying to twice their initial values ten years earlier. The urban informal land use class also experienced its subtle growth throughout the timeline of the study with its own urban expansion also happening between 1986 and 1995 with double increase in CA, NP and ED metric values. However, unlike the formal class that experienced decline after this period of urban expansion, the informal class continued to experience growth up until the end of the study period. Infrastructural violence was measured using the fractal dimension index (AWMPFD) of the landscape metrics for the formal and informal LU class. The results reveal continuous fragmentation throughout the period of study but with higher values in the years in which urban development started. / LG2018

No description available.

<p>The LiDAR data of the maize used in this research were acquired from different stages, by different sensors, and from different flight heights, which results in different point densities. The ground reference data collected by LiCOR LAI-2200 represented the leaf area index of a two-row plot.</p>

No description available.

Bibliography: leaves 176-179.

Includes bibliographical references (leaves 176-179) / xiii, 202 leaves : ill. (some col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, 1989