Mapping Spatio-Temporal Cropland Changes Due To Water Stress In Krishna River Basin Using Temporal Satellite Data

Natural hazards namely droughts, floods, cyclones, hailstorms, volcanic eruptions, earth quakes, landslides, forest fire, locust outbreak etc, are common on the earth’s surface. Most of them are of climatic origin. Incidence of these hazards causes loss of human life, failure of crops and destruction of ecosystems. Consequently, the social as well as economic conditions of any region is disoriented. Natural hazards cannot be prevented but the loss can be minimized to some extent by taking appropriate disaster mitigation strategies. These strategies can be achieved by developing early warning systems and developing effective communication systems to take immediate action during the incidence of disasters, improving medical services and training to the people individually; how to react when disaster warning announced in a region, on their own without waiting for the help. Thus, disaster management includes warning, prevention, planning, preparedness, monitoring, and assessment and relief activity

Assessment of high resolution SAR imagery for mapping floodplain water bodies: a comparison between Radarsat-2 and TerraSAR-X

Flooding is a world-wide problem that is considered as one of the most devastating natural hazards. New commercially available high spatial resolution Synthetic Aperture RADAR satellite imagery provides new potential for flood mapping. This research provides a quantitative assessment of high spatial resolution RADASAT-2 and TerraSAR-X products for mapping water bodies in order to help validate products that can be used to assist flood disaster management. An area near Dhaka in Bangladesh is used as a test site because of the large number of water bodies of different sizes and its history of frequent flooding associated with annual monsoon rainfall. Sample water bodies were delineated in the field using kinematic differential GPS to train and test automatic methods for water body mapping. SAR sensors products were acquired concurrently with the field visits; imagery were acquired with similar polarization, look direction and incidence angle in an experimental design to evaluate which has best accuracy for mapping flood water extent. A methodology for mapping water areas from non-water areas was developed based on radar backscatter texture analysis. Texture filters, based on Haralick occurrence and co-occurrence measures, were compared and images classified using supervised, unsupervised and contextual classifiers. The evaluation of image products is based on an accuracy assessment of error matrix method using randomly selected ground truth data. An accuracy comparison was performed between classified images of both TerraSAR-X and Radarsat-2 sensors in order to identify any differences in mapping floods. Results were validated using information from field inspections conducted in good conditions in February 2009, and applying a model-assisted difference estimator for estimating flood area to derive Confidence Interval (CI) statistics at the 95% Confidence Level (CL) for the area mapped as water. For Radarsat-2 Ultrafine, TerraSAR-X Stripmap and Spotlight imagery, overall classification accuracy was greater than 93%. Results demonstrate that small water bodies down to areas as small as 150m² can be identified routinely from 3 metre resolution SAR imagery. The results further showed that TerraSAR-X stripmap and spotlight images have better overall accuracy than RADARSAT-2 ultrafine beam modes images. The expected benefits of the research will be to improve the provision of data to assess flood risk and vulnerability, thus assisting in disaster management and post-flood recovery

Investigating the groundwater dependence and response to rainfall variability of vegetation in the Touws river and catchment using remote sensing

Magister Artium - MAChanges in climate patterns have raised concerns for environmentalists globally and across southern Africa. The changes greatly affect the growth dynamics of vegetation to such an extent that climate elements such as rainfall have become the most important determinant of vegetation growth. In arid and semi-arid environments, vegetation relies on near-surface groundwater as the main source of water. Changes in the environment due to climate can be examined by using remotely sensed data. This approach offers an affordable and easy means of monitoring the impact of climate variability on vegetation growth. This study examined the response of vegetation to rainfall and temperature, and assessed the dependence thereof on groundwater in a climatically variable region of the semi-arid Karoo. The methodology used included sampling plant species in the riparian and non-riparian areas over two plant communities in seven vegetation plots. The Normalised Difference Vegetation Index (NDVI) derived from the Landsat OLI and TM was used to measure vegetation productivity. This was compared with rainfall totals derived from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the mean monthly temperature totals. A drought index, (Standardised Precipitation Index – SPI) was an additional analysis to investigate rainfall variability. Object-based Image Analysis (OBIA) and Maximum Likelihood supervised classification approaches together with indicators of groundwater discharge areas (Topographic Wetness Index – TWI, and profile curvature) were used to map vegetation and surface water that depend on groundwater

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects

Bayesian spatio-temporal modelling for malaria surveillance and residual pockets of transmission identification in Swaziland

Malaria control has been and is in the world spotlight for over 50 years and had been marked by a proliferation of research studies, as interest in finding new control methods increased. Along the full spectrum from malaria control to prevention of reintroduction emphasis on surveillance and response has been made if gains in the fight against the disease are to be realized. International funding for anti-malaria related activities has also been up-scaled and sustained with a significant amount of it focusing on the high burden countries in the sub-Saharan region of continental Africa. Understanding the complex interactions between malaria vectors, parasites and human hosts is key to control and elimination of the disease. Geostatistical methods involving the use of remote sensing (RS) techniques and geographic information system (GIS) tools have proven to be an effective way of estimating spatial and temporal effects of environmental determinants on disease outcomes. They also allow us to produce model-based maps which could be used to predict the disease at explicit geographic scales thus aiding targeted control. In the context of surveillance, preparedness and response we explored potential methods and tools that could be used for surveillance by malaria control programmes in very low endemic settings like Swaziland. In this country, malaria has drastically declined and the country is currently in its elimination stage as it entered the critical 3-year phase from 2015 to 2018 where it is anticipated that it will receive certification from the World Health Organization (WHO) as a malaria free country. Spatially explicit maps on micro-epidemiological heterogeneities as well as space and time trends and patterns in malaria transmission are needed to aid the country to target and prioritize interventions in this critical phase as it deals with individual episodic cases. Currently achieving malaria elimination remains operationally challenging due to the ever present threat of imported cases from nearby endemic regions and from uncensored immigration. Also the turnaround time from data collection, processing and use for planning purposes is too long for rapid response actions. Therefore a rapid response surveillance system is needed in order to achieve elimination and prevent reintroduction after elimination. Chapter 1 presents the overall background informing this study including the rationale for undertaking this PhD work. The role of surveillance in malaria control and elimination as well as the importance of rapid response in malaria elimination were also presented. We showed the progress the country has made from the establishment of the malaria control unit in the 1940s to present time. Our study focused on the use of environmental data for disease surveillance. Therefore we detailed the environmental factors associated with malaria transmission and demonstrated how they were interlinked with disease incidence. Such factors included temperature, precipitation and humidity. The use of earth observation (EO) data derived from RS techniques was also presented. Tools that could be used to support surveillance such as GIS and global positioning systems (GPS) are also discussed. We look at the current malaria situation in Swaziland with emphasis on the latest developments following the scaling up of malaria interventions in that country. In chapter 2 we emphasised the importance of mapping potential vector breeding sites in Swaziland using high resolution remotely sensed data in conjunction with entomological data to aid larval source management (LSM) strategies. We used larva scooping methods to identify potential breeding sites in the country and those identified were fed into a decision tree induction algorithm and a logistic regression to assess which environmental factors characterised larvae presence or absence. Both approaches reliably distinguished between the two set of scenarios of larvae presence or absence and identified the same environmental predictor related to human activity (subsistence farming) as key determinant of potential vector breeding. Models linking presence of larvae with high resolution land-use variables were found to have good predictive ability. Thus we produced a map of predicted potential breeding sites at explicit geographic scales to assist the malaria control programme in planning its LSM budget. There are many environmental proxies that have been proposed by ecologists and remote sensing experts which have a potential for use in vector-borne disease mapping. However, their uptake by epidemiologists has remained notoriously slow. Therefore in chapter 3 we investigated the litany of available RS variables that could be used in vector-borne disease mapping studies. We reviewed literature on available sources of remotely sensed data and presented a library of supplier processed variables and those that need to be derived by the end-users and processed at different levels before being incorporated into disease mapping studies. We discussed the reasons and criteria used to select the proxies described and presented in our catalogue. Indices investigated were limited to those related to EO data products with continental or global coverage scales, and were grouped according to meteorology, land use/cover, cartography and urban mapping variables which could be used as proxies for disease suitability mapping. We found numerous indices that have been derived by ecologists and remote sensing experts from the various satellite sensors that have been launched over the years. However, they have remained underutilized in epidemiology partly because of lack of remote sensing skills needed to derive them and partly because they were not high demand variables and therefore not provided by remote sensing agents and suppliers of remotely sensed data. In chapter 4, we explored different scenarios for malaria incidence risk by investigating the environmental effects of weekly distributed lags in Swaziland. A Bayesian geostatistical model based on polynomial distributed lags function was developed to assess how different environmental and socio economic factors influenced malaria incidence in the country. We then produced model based spatially explicit maps of predicted malaria incidence risk which could be used by the control programme to target their control interventions for high impact. In chapter 5, we evaluated some of the new and potential indices for epidemiological studies by testing and comparing their use in predicting malaria incidence risk in Swaziland. We discussed the inclusion criteria and choice of the selected variables for malaria incidence risk prediction in the country. This was necessitated by the fact that new satellites have been launched with much improved sensor capabilities than previous first generation sensors. Sensor improvements are noticeable in the number of spectral bands, spatial and temporal resolutions, thus presenting unprecedented good image sources for identification of spatial heterogeneities, trends and patterns in disease mapping by epidemiologists. We ended with emphasising the importance of why this research work was carried out including discussing the key findings and overall message that came from this study. The contributions that had been made by this study are also discussed as well as remaining research work that could be undertaken as follow up

The estimation and evaluation of a satellite-based drought index using rainfall and evapotranspiration.

Master of Science in Hydrology. University of KwaZulu-Natal. Pietermaritzburg, 2017.Abstract available in PDF file

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

Planet Earth 2011

The failure of the UN climate change summit in Copenhagen in December 2009 to effectively reach a global agreement on emission reduction targets, led many within the developing world to view this as a reversal of the Kyoto Protocol and an attempt by the developed nations to shirk out of their responsibility for climate change. The issue of global warming has been at the top of the political agenda for a number of years and has become even more pressing with the rapid industrialization taking place in China and India. This book looks at the effects of climate change throughout different regions of the world and discusses to what extent cleantech and environmental initiatives such as the destruction of fluorinated greenhouse gases, biofuels, and the role of plant breeding and biotechnology. The book concludes with an insight into the socio-religious impact that global warming has, citing Christianity and Islam

A spatially explicit methodology for assessing and monitoring land degradation neutrality at a national scale

Land degradation is occurring in all parts of the terrestrial world, and is negatively impacting the well-being of billions of people. In recognition of the need for sustained global action on land degradation, the Sustainable Development Goals, adopted by the global community in 2015, include a specific goal aimed at halting the decline of land resources and achieving land degradation-neutrality (LDN) by 2030. The primary objective of this doctoral research was to operationalise the LDN target at the national level, using Kenya as the case study. The main research questions addressed in this dissertation have been positioned within a social-ecological systems framework in which ecosystems are integrated with human society. The first task of this research focused on determining the extent of land degradation and regeneration, and in establishing the LDN national baseline using the three LDN indicators (land cover, land productivity, and carbon stocks). This was then followed by identifying the key drivers that affect land degradation (browning) and land regeneration (greening) trends within the 4 main land cover types (agriculture, forest, grassland and shrubland), and within an area characterised by land cover change. The third task involved an assessment of the effectiveness of the current land-use policy framework, and associated institutions, to facilitate the implementation of LDN. Finally, in the last part of this dissertation, a climate-smart landscape approach at the water catchment level was proposed as a possible mechanism through which LDN can be operationalised at the sub-national level; Metodologia espacialmente explicita para a avaliação e monitorização da neutralidade da degradação do solo à escala nacional RESUMO: A degradação do solo é um fenómeno que está a acontecer em todas as partes do mundo terrestre, com impactos negativos no bem estar de milhares de milhões de pessoas. Reconhecendo a necessidade de uma ação global contra a degradação do solo, os Objetivos de Desenvolvimento Sustentável, adotados pela comunidade global em 2015, incluem um objetivo específico para travar o declínio de recursos terrestres e atingir a neutralidade de degradação do solo (NDS) até 2030. A presente tese de doutoramento teve como grande objetivo a operacionalização da NDS a nível nacional, usando o Quénia como caso de estudo. As principais perguntas de investigação consideradas nesta dissertação foram colocadas num enquadramento socio-ecológico, em que ecossistemas estão integrados com a sociedade. A primeira tarefa desta investigação consistiu em determinar valores de degradação e de regeneração do solo para estabelecer a base de referência de NDS nacional usando três indicadores de NDS (cobertura de solo, produtividade do solo e reservas de carbono). Seguidamente foram identificados os principais fatores que influenciam a degradação do solo (browning) e a regeneração do solo (greening) nas 4 principais coberturas de solo (agricultura, floresta, pastos e matos), bem como numa área marcada por alterações da cobertura de solo. Para a terceira tarefa foi avaliada a eficácia do atual quadro político sobre o uso de solo, bem como das instituições associadas, na viabilização da implementação da NDS. Na última parte da dissertação é adotada uma escala a nível da bacia hidrográfica, como uma abordagem “climate smart” adequada para a operacionalização da NDS a um nível sub-nacional