Quantifying the impact of the Land Reform Programme on land use and land cover changes in Chipinge District, Zimbabwe, based on Landsat observations

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science (Geographical Information Systems and Remote Sensing) at the School of Geography, Archaeology & Environmental Studies. Johannesburg, 2016.The purpose of this research was to quantify the impact of the land reform programme on land use and land cover changes (LULCC) in Chipinge district situated in Manicaland Province of Zimbabwe. The Fast Track Land Reform Programme (FTLRP) of 2000 was selected as the major cause of LULCC in the district. This research addresses the problem of knowing and understanding if there was LULCC in the district before and after the enactment of the FTLRP in the year 2000. The research objectives of this study were as follows: to investigate the impact of the FTLRP of 2000 on land use and land cover in Chipinge district; to test the use of Landsat earth observation data in quantifying the changes on land use and cover from 1992 to 2014 in Chipinge district and to predict LULCCs in the year 2028 in Chipinge district. The methodology for detecting the impact of LULCC was based on the comparison of Landsat MSS, TM, ETM+ and OLI/ TIRS scene p168r74 images covering Chipinge district taken on diverse dates in five different years. In order to prepare the Landsat images for change detection analysis, a number of image processing operations were applied which include radiometric calibration and atmospheric correction. The images were classified using the Support Vector Machine (SVM) and evaluation was done through accuracy assessment using the confusion matrix. The prediction of LULCC in the year 2028 was modeled by the Markov Chain Analysis (MCA) and the Cellular Automata Markov Chain Analysis (CA MCA) so as to show land distribution in the future. The results show that agricultural farmland, estates and area covered by water bodies declined whilst there was an increase in built-up areas, forest land and bare land since the enactment of the FTLRP. The prediction results show that in the year 2028, there will be a decrease in the amount of land covered by water bodies, forest and agricultural farmland. There will be an increase in the amount of built-up in the year 2028 as a result of population growth. It is the recommended in this study that better remedies be put in place to increase forest cover and also the use of high resolution images in further studies. There should be exploration of the relationships between LULCC, socio-economic and demographic variables would develop more understanding of LULCC. The study also recommends the preparation of a proper land use plan to deal with a reduction in the growth of settlement which is vital in the planning and management of social and economic development programs.LG201

Exploring the Potential of Feature Selection Methods in the Classification of Urban Trees Using Field Spectroscopy Data

Mapping of vegetation at the species level using hyperspectral satellite data can be effective and accurate because of its high spectral and spatial resolutions that can detect detailed information of a target object. Its wide application, however, not only is restricted by its high cost and large data storage requirements, but its processing is also complicated by challenges of what is known as the Hughes effect. The Hughes effect is where classification accuracy decreases once the number of features or wavelengths passes a certain limit. This study aimed to explore the potential of feature selection methods in the classification of urban trees using field hyperspectral data. We identified the best feature selection method of key wavelengths that respond to the target urban tree species for effective and accurate classification. The study compared the effectiveness of Principal Component Analysis Discriminant Analysis (PCA-DA), Partial Least Squares Discriminant Analysis (PLS-DA) and Guided Regularized Random Forest (GRRF) in feature selection of the key wavelengths for classification of urban trees. The classification performance of Random Forest (RF) and Support Vector Machines (SVM) algorithms were also compared to determine the importance of the key wavelengths selected for the detection of the target urban trees. The feature selection methods managed to reduce the high dimensionality of the hyperspectral data. Both the PCA-DA and PLS-DA selected 10 wavelengths and the GRRF algorithm selected 13 wavelengths from the entire dataset (n = 1523). Most of the key wavelengths were from the short-wave infrared region (1300-2500 nm). SVM outperformed RF in classifying the key wavelengths selected by the feature selection methods. The SVM classifier produced overall accuracy values of 95.3%, 93.3% and 86% using the GRRF, PLS-DA and PCA-DA techniques, respectively, whereas those for the RF classifier were 88.7%, 72% and 56.8%, respectively

Remote Sensing Application in Mountainous Environments: A Bibliographic Analysis

Advancement in remote sensing platforms, sensors, and technology has significantly improved the assessment of hard-to-access areas, such as mountains. Despite these improvements, Africa lags in terms of research work published. This is of great concern as the continent needs more research to achieve sustainable development. Therefore, this study applied a bibliometric analysis of the annual production of publications on the application of remote sensing methods in mountainous environments. In total, 3849 original articles between 1973 and 2021 were used, and the results indicate a steady growth in publications from 2004 (n = 26) to 2021 (n = 504). Considering the source journals, Remote Sensing was the top-ranked, with 453 total publications. The University of the Chinese Academy of Sciences was the highest-ranking affiliation, with 217 articles, and China produced the highest number of publications (n = 217). Keywords used between 1973 and 1997, such as “Canada”, “alps”, and “GIS”, metamorphosed into “remote sensing” between 1998 and 2021. This metamorphosis indicates a change in the areas of interest and an increase in the application of remote sensing methods. Most studies were conducted in the Global North countries, and a few were published in low-impact journals within the African continent. This study can help researchers and scholars better understand the progress and intellectual structure of the field and future research directions in the application of remote sensing methods in mountainous environments

Classification of urban tree species using LiDAR data and WorldView-2 satellite imagery in a heterogeneous environment

Feature complexity and heterogeneity of urban areas pose a challenge for tree species classification. This study examined the effectiveness of the integrated Worldview-2 (WV-2) bands, vegetation indices and normalized Digital Surface Model (nDSM) dataset in mapping common urban tree species and other land use and land cover (LULC) types using Random Forest (RF) and Support Vector Machine (SVM) algorithms. The study also ranked the importance of nDSM, WV-2 bands and vegetation indices. The results indicate that the integrated dataset was effective as shown by high classification accuracies of 97% for the RF and 94% for SVM classifiers. The nDSM was the top-ranked variable with high Mean Decrease in Accuracy (MDA) and Mean Decrease in Gini (MDG) scores of 0.98 and 0.61, respectively. This research provides information to municipalities on the methods and data that can be used for the sustainable management of urban tree species

Evaluating the capability of Worldview-2 imagery for mapping alien tree species in a heterogeneous urban environment

Street trees in urban planning have a long history as providers of an amicable environment for urban dwellers. Nevertheless, street trees are not always without a challenge, their ecosystem disservices include, inter alia, cracking pavements and foundations due to wandering tree roots that destroy concrete or asphalt surfaces. Thus, effective mapping of street trees assists in planning a suitable urban environment to improve city life. The traditional method for urban tree mapping is costly, time-consuming and labour intensive. However, commercially operated multi-spectral sensors, such as WorldView (WV) provide a more viable way to map trees at the species level. This study investigates the use of WV-2 imagery in the classification and mapping of five common alien street trees in a complex urban environment. It also examined the feasibility of Random Forest (RF) and Support Vector Machines (SVM) classifiers in mapping street trees in a heterogeneous urban environment. The classifiers produced an overall accuracy of 84.2 % for RF and 81.2 % for SVM. This study provides a detailed understanding of urban tree species to the municipality of Johannesburg and offers environmental managers an insight of classification methods for mapping trees using satellite imagery to comprehend their spatial distribution