Fire scar mapping for disaster response in KwaZulu-Natal South Africa using Landsat 8 imagery

This study assessed the potential of the new Landsat 8 multispectral imagery in rapidly mapping fire scars to aid disaster management response teams in emergency efforts. Maximum likelihood and iso cluster algorithms where used to classify burnt and unburnt areas in KwaZulu-Natal, South Africa. The Landsat 8 sensor successfully classified burnt and unburnt areas with overall accuracies ranging from 80% to 93.33% on independent test datasets. Farms and communities affected by the wildfires were overlaid with the classified maps in order to determine the extent of each farm burnt. Maps were created for disaster management response teams in order to identify critical farms and communities in need of assistance. The study indicates the operational use of the new Landsat 8 data in fire scar mapping for disaster response. The result is critical for fire scar mapping in South Africa using freely available Landsat 8 multispectral data

Quantifying the physical composition of urban morphology throughout Wales based on the time series (1989-2011) analysis of Landsat TM/ETM+ images and supporting GIS data

Knowledge of impervious surface areas (ISA) and on their changes in magnitude, location, geometry and morphology over time is significant for a range of practical applications and research alike from local to global scales. Despite this, use of Earth Observation (EO) technology in mapping ISAs within some European Union (EU) countries, such as the United Kingdom (UK), is to some extent scarce. In the present study, a combination of methods is proposed for mapping ISA based on freely distributed EO imagery from Landsat TM/ETM+ sensors. The proposed technique combines a traditional classifier and a linear spectral mixture analysis (LSMA) with a series of Landsat TM/ETM+ images to extract ISA. Selected sites located in Wales, UK, are used for demonstrating the capability of the proposed method. The Welsh study areas provided a unique setting in detecting largely dispersed urban morphology within an urban-rural frontier context. In addition, an innovative method for detecting clouds and cloud shadow layers for the full area estimation of ISA is also presented herein. The removal and replacement of clouds and cloud shadows, with underlying materials is further explained. Aerial photography with a spatial resolution of 0.4 m, acquired over the summer period in 2005 was used for validation purposes. Validation of the derived products indicated an overall ISA detection accuracy in the order of ~97%. The latter was considered as very satisfactory and at least comparative, if not somehow better, to existing ISA products provided on a national level. The hybrid method for ISA extraction proposed here is important on a local scale in terms of moving forward into a biennial program for the Welsh Government. It offers a much less subjectively static and more objectively dynamic estimation of ISA cover in comparison to existing operational products already available, improving the current estimations of international urbanization and soil sealing. Findings of our study provide important assistance towards the development of relevant EO-based products not only inaugurate to Wales alone, but potentially allowing a cost-effective and consistent long term monitoring of ISA at different scales based on EO technology

Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way

Food industry site selection using geospatial technology approach

Food security has been an ongoing concern of governments and international organizations. One of the main issues in food security in Developing and Sanctioned Countries (DSCs) is establishment of food industries and related distributions in appropriate places. In this respect, geospatial technology offers the most up-to-date Land Cover (LC) information to improve site selection for assisting food security in the study area. Currently food security issues are not comprehensively addressed, especially in DSCs. In this research, ASTER L1B and LANDSAT satellite data were used to derive various LC biophysical parameters including build-up area, water body, forest, citrus, and rice fields in Qaemshahr city, Iran using different satellite-derived indices. A Product Level Fusion (PLF) approach was implemented to merge the outputs of the indices to prepare an improved LC map. The suitability of the proposed approach for LC mapping was evaluated in comparison with Support Vector Machine (SVM) and Artificial Neural Network (ANN) classification techniques. For implementing site selection, the outcomes of satellite-derived indices, as well as the city, village, road, railway, river, aqueduct, fault, casting, abattoir, cemetery, waste accumulation, wastewater treatment, educational centre, medical centre, military centre, asphalt factory, cement factory, and slope layers were obtained using Global Positioning System (GPS), on-screen digitizing, and image processing were used as input data. The Fuzzy Overlay and Weighted Linear Combination (WLC) methods were adopted to perform site selection process. The outcomes were then classified and analyzed based on the accessibility to main roads, cities and raw food materials. Finally, the existing industrial zones in the study area were evaluated for establishing food industries based on site selection results of this study. The results indicated higher performance of PLF method to provide up-to-date LC information with an overall accuracy and Kappa coefficient values of 95.95% and 0.95, respectively. The site selection result obtained using WLC method with the accuracy of 90% was superior, thus it was selected for further analyses. Based on the achieved results, the study has proven the applicability of current satellite data and geospatial technology for food industry site selection to resolve food security issues. In conclusion, site selection using geospatial technology provides a great potential for a reliable decision-making in food industry planning, as a significant issue in agro-based food security, especially in sanctioned countries

Spatial modeling of plant distributions: coupling remote sensing with GIS-based models

Spatial species distributions and the relationship between species and environmental factors have been studied for several years. Climate change and habitat fragmentation can be considered as the factors effective in biodiversity changes. Therefore prediction of species range shifts under climate change and other physical processes is a crucial challenge for the management of natural resources. The major objective of this thesis was to integrate MigClim, SDM and CA-Markov chain models so as to assess the effects of future landscape fragmentation and climate change scenarios on the geographic distributions of three open-land plant species in 21st century. For all target plants, simulations were performed for four dispersal events (full dispersal, no dispersal, regular dispersal (short-distance dispersal), and regular dispersal along with long-distance dispersal), two landscape (static and dynamic change) and two climate change (RCP4.5 and RCP8.5) scenarios (chapter 5). In this investigation, it was shown that the predicted distribution areas for all the three species under RCP8.5 scenario will largely increase in the coming decades. Also, a significant difference appears to be between the simulations of realistic dispersal limitations and those considering full or no dispersal for projected future distributions during the 21st century. Besides, the results obtained by the limited projections of future plant distributions via realistic dispersal restrictions showed to be generally closer to no-dispersal than to full-dispersal scenario when compared with real dispersal scenarios. Overall, the results of this study indicate that dispersal limitations can have an important impact on the outcome of future projections of species distributions under climate change scenarios. Also our findings clearly showed that change in landscape fragmentation is more effective than the climate change impacts on species distributions in our study area

Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file

Uso combinado de técnicas de teledetección y modelización para evaluar la distribución vertical de la vegetación

El objetivo de esta tesis es diseñar metodologías que identifiquen la distribución vertical de la vegetación independientemente de los ecosistemas que componen la zona de estudio. Esta tesis, al enmarcarse en un doctorado industrial, incorpora el objetivo de crear desarrollos metodológicos que impulsen la competitividad de la empresa en el mercado laboral. Con este objetivo último, la tesis ha implementado tres metodologías ágiles, precisas y aplicables a gran escala, cuya finalidad ha sido identificar la estructura tridimensional de la vegetación que condiciona las coberturas del suelo presentes y, por tanto, la gestión aplicable en cada caso. Esta tesis incorpora tres capítulos, independientes entre sí, cada uno con un reto metodológico. El Capítulo 3 buscó detectar árboles individuales de plantaciones jóvenes de Pinus pinaster y Pinus radiata en parcelas permanentes destinadas a investigación, delinear sus copas y evaluar su altura. El Capítulo 4 incluye una metodología capaz de identificar las coberturas de suelo presentes en una interfaz urbano-forestal, las cuales están relacionadas directamente con la distribución vertical de la vegetación. El Capítulo 5 incluye el desarrollo de una metodología para identificar un umbral capaz de adaptarse a la estructura real de cada parcela. Este umbral sería una alternativa generalizable a todo un monte y replicable sin necesidad de nuevas mediciones en campo. Paralelamente a estos trabajos se ha participado en dos registros de la propiedad intelectual, easyLaz® y easySat®, cuya finalidad es el procesado de información LiDAR y multiespectral respectivamente