Detecting the Presence of Disease by Unifying Two Methods of Remote Sensing.

There is currently no effective tool available to quickly and economically measure a change in landmass in the setting of biomedical professionals and environmental specialists. The purpose of this study is to structure and demonstrate a statistical change-detection method using remotely sensed data that can detect the presence of an infectious land borne disease. Data sources included the Texas Department of Health database, which provided the types of infectious land borne diseases and indicated the geographical area to study. Methods of data collection included the gathering of images produced by digital orthophoto quadrangle and aerial videography and Landsat. Also, a method was developed to identify statistically the severity of changes of the landmass over a three-year period. Data analysis included using a unique statistical detection procedure to measure the severity of change in landmass when a disease was not present and when the disease was present. The statistical detection method was applied to two different remotely sensed platform types and again to two like remotely sensed platform types. The results indicated that when the statistical change detection method was used for two different types of remote sensing mediums (i.e.-digital orthophoto quadrangle and aerial videography), the results were negative due to skewed and unreliable data. However, when two like remote sensing mediums were used (i.e.- videography to videography and Landsat to Landsat) the results were positive and the data were reliable

Exploring agricultural land-use and childhood malaria associations in sub-Saharan Africa

Agriculture in Africa is rapidly expanding but with this comes potential disbenefits for the environment and human health. Here, we retrospectively assess whether childhood malaria in sub-Saharan Africa varies across differing agricultural land uses after controlling for socio-economic and environmental confounders. Using a multi-model inference hierarchical modelling framework, we found that rainfed cropland was associated with increased malaria in rural (OR 1.10, CI 1.03 – 1.18) but not urban areas, while irrigated or post flooding cropland was associated with malaria in urban (OR 1.09, CI 1.00 – 1.18) but not rural areas. In contrast, although malaria was associated with complete forest cover (OR 1.35, CI 1.24 – 1.47), the presence of natural vegetation in agricultural lands potentially reduces the odds of malaria depending on rural-urban context. In contrast, no associations with malaria were observed for natural vegetation interspersed with cropland (veg-dominant mosaic). Agricultural expansion through rainfed or irrigated cropland may increase childhood malaria in rural or urban contexts in sub-Saharan Africa but retaining some natural vegetation within croplands could help mitigate this risk and provide environmental co-benefits

Impact of land use change on urban surface temperature and urban green space planning; case study of the island of Bali, Indonesia

Land use and surface temperature were monitored from 1995 to 2013 to examine green space development in Bali using Landsat and ASTER imageries. Urban areas were formed by conversion of vegetation and paddy fields. Heat islands with surface temperature of over 29 ºC were found and influenced by urban area types. High priority, low priority and not a priority zones for green space were resulted by weighted overlay of LST, NDVI and urban area types

Biogeographic classification of the Caspian Sea

Like other inland seas, the Caspian Sea (CS) has been influenced by climate change and anthropogenic disturbance during recent decades, yet the scientific understanding of this water body remains poor. In this study, an eco-geographical classification of the CS based on physical information derived from space and in situ data is developed and tested against a set of biological observations. We used a two-step classification procedure, consisting of (i) a data reduction with self-organizing maps (SOMs) and (ii) a synthesis of the most relevant features into a reduced number of marine ecoregions using the hierarchical agglomerative clustering (HAC) method. From an initial set of 12 potential physical variables, 6 independent variables were selected for the classification algorithm, i.e., sea surface temperature (SST), bathymetry, sea ice, seasonal variation of sea surface salinity (DSSS), total suspended matter (TSM) and its seasonal variation (DTSM). The classification results reveal a robust separation between the northern and the middle/southern basins as well as a separation of the shallow nearshore waters from those offshore. The observed patterns in ecoregions can be attributed to differences in climate and geochemical factors such as distance from river, water depth and currents. A comparison of the annual and monthly mean Chl <i>a</i> concentrations between the different ecoregions shows significant differences (one-way ANOVA, <i>P</i> < 0.05). In particular, we found differences in phytoplankton phenology, with differences in the date of bloom initiation, its duration and amplitude between ecoregions. A first qualitative evaluation of differences in community composition based on recorded presence–absence patterns of 25 different species of plankton, fish and benthic invertebrate also confirms the relevance of the ecoregions as proxies for habitats with common biological characteristics

Detection, monitoring and management of small water bodies:: A case study of Shahjadpur Thana, Sirajgonj district, Bangladesh

Bangladesh is a low-lying flood prone deltaic plain. Excavations are needed to create raised land for safe flood-free homesteads and water bodies for irrigation, and these result in the creation of doba, pukur, dighi and jola. All of these types of small water bodies are almost equally distributed all over the country, except for the heel, which is a natural, saucer shaped depression. For every eight people there is approximately an acre of small water bodies, which range in size from 25-400 sq.m. (doba), 150-1000 sq.m. (pukur), >750 sq.m. (dighi), >2000 sq.m. (jola) and >1000 sq.m. (heel). These small water bodies are commonly used for drinking, bathing and washing, fisheries and aquaculture, duck raising, irrigation, cattle feeding and washing. Despite the importance of small water bodies to the local economy there is no up to date inventory. For this purpose, in my research I have employed integrated participatory remote sensing, GIS and socio-cultural approaches. Although these have not been used before in Bangladesh, 1 argue that they are ideal for effective resource management and sustainable development planning. This research investigated the historical development of the present spatial distribution and use patterns of SWB using Remote Sensing and GIS. This was at a regional scale in four mouzas of Shahjadpur Thana. The data sources were topographical maps, aerial photographs, satellite images, agricultural census data, in-depth questionnaire, focus group meetings and interviewing key informants. An integrated RS-GIS and social sciences methodology was employed to produce maps of change and overlays of the socio-cultural factors involved. Results show that the doba, pukur and dighi, when these are not obstructed by surrounding vegetation, can be detected easily in high resolution panchromatic CORONA satellite photography, IRS-ID Panchromatic image and aerial photography. Comparatively large pukurs, dighis and all jo las and heels are detected in all other optical sensors and the SIR-C radar imagery. Multi-temporal images are helpful for identifying the different types of small water bodies as well separating those from other seasonal large water bodies and flooded areas. It is hoped that the proposed computer assisted participatory management system, including some locally specific guidelines, may be applicable for the planning of other thanas (total 490) in Bangladesh. The proposed management system will facilitate the integration of local planning with the national level planning process, which has not been possible hitherto

Analysis of the geographical patterns of malaria transmission in KwaZulu-Natal, South Africa using Bayesian spatio-temporal modelling.

M. Sc. University of KwaZulu-Natal, Durban 2013.Malaria is one of the most important public health issues that is still affecting millions of people around the world, especially in Africa. Africa accounted for 80% of the 216 million cases worldwide and 91% of deaths. It poses serious economic burdens on communities and countries at large. However, through temporal and spatial mapping of the disease populations at risk can be identified timeously and resources distributed accordingly. Since malaria is a climatic disease geostatistical approaches can be utilised in modelling its spatial distribution. Bayesian geostatistical methods enable the mathematical descriptions of the environment-disease association. Significant environmental predictors of malaria transmission can be identified which can also allow for the development of a malaria epidemic prediction model. This model can serve as a surveillance system for early detection and containment of the disease. Therefore, it is crucial to understand the complex dynamics of malaria transmission so malaria control programmes can be more effective and efficient in managing this public health issue. In South Africa, malaria is transmitted in 3 provinces: KwaZulu-Natal, Mpumalanga and Limpopo. Although malaria is highly seasonal in these areas and KwaZulu-Natal has experienced tremendous achievements in decreasing morbidity and mortality due to malaria, it still remains in an unstable condition that needs constant control and surveillance. The aim of this study was to investigate which environmental/climatic variables are drivers of malaria incidence in KwaZulu-Natal and subsequently develop methods to produce risk maps using Bayesian spatio-temporal modelling. It emerged from the research that the main environmental/climatic drivers of malaria incidence in KwaZulu-Natal were the day temperature of the previous month, altitude and forest land cover type. This was due to the different ways these three factors affect the three-way interaction of the vector, the parasite and the human host. The predicted risk maps showed that incidence rates ranged from 0.2 to 5 per 1000 inhabitants in the study area. This prediction was based on only the climatic factors, however, non-climatic factors also affect malaria transmission through vector control strategies like Indoor Residual Spraying among others

Citizen Science and Geospatial Capacity Building

This book is a collection of the articles published the Special Issue of ISPRS International Journal of Geo-Information on “Citizen Science and Geospatial Capacity Building”. The articles cover a wide range of topics regarding the applications of citizen science from a geospatial technology perspective. Several applications show the importance of Citizen Science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities, which are covered in the book. Particular emphasis is given to various problems encountered in the CitSci and VGI projects with a geospatial aspect, such as platform, tool and interface design, ontology development, spatial analysis and data quality assessment. The book also points out the needs and future research directions in these subjects, such as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders

Analysis of geographical and temporal patterns of malaria transmission in Limpopo Province, South Africa using Bayesian geo-statistical modelling.

M.Sc. University of KwaZulu-Natal, Durban, 2013.South Africa is at the southern fringe of sub-Saharan African countries which persist in experiencing malaria transmission. The purpose of the study is to analyse the geographical and temporal patterns of malaria transmission from 2000 to 2011 using Bayesian geostatistical modelling in Limpopo Province, South Africa. Hereafter, develop malaria case data-driven spatio-temporal models to assess malaria transmission in Limpopo Province. Malaria case data was acquired from the South African Medical Research Council (MRC). Population data was acquired from AfriPopo; and Normalised Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Land Cover data were acquired from MODerate-resolution Imaging Spectro-radiometer (MODIS). Rainfall, Altitude and distance to water bodies’ data were acquired from African Data Dissemination Service (ADDS), United States Geological Survey (USGS) and Environmental Systems Research Institute (ESRI), respectively. Bayesian spatio-temporal incidence models were formulated for Gibbs variable selection and models were fitted using the best set of environmental factors. Modelbased predictions were obtained over a regular grid of 1 x 1km. spatial resolution covering the entire province and expressed as rates of per 1 000 inhabitants for the year 2010. To assess the performance of the predicted malaria incidence risk maps, the predictions and field observations were compared. The best set of environmental factors selected by variable selection was Altitude and the night temperature of two months before the case was reported. The environmental factors were then used for model fitting and all of the covariates were important on malaria risk. Predictions were done using all the environmental factors. The predictions showed that Vhembe and Mopani district municipalities have high malaria transmission as compared to other district municipalities in Limpopo Province. Assessment of predictive performance showed scatter plots with the coefficient of determination ( R² ). The values representing the statistical correlation represented by the coefficient of determination ( R² ) were 0.9798 (January), 0.8736 (February), 0.8152 (March), 0.8861 (April), 0.9949 (May), 0.3838 (June), 0.7794 (July), 0.9235 (September), 0.8966 (October), 0.9834 (November) and 0.8958 (December). August had two values reported and predicted which resulted in R² of 1. The numbers of the The produced malaria incidence maps can possibly be considered as one of the baselines for future malaria control programmes. The results highlighted the risk factors of malaria in Limpopo Province which are the most important characteristics of malaria transmission