Avaliação da evolução do índice de vegetação de teledetecção usando de técnicas de processamento de imagens

Vegetation has a substantial role as an indicator of anthropic effects, specifically in cases where urban planning is required. This is especially the case in the management of coastal cities, where vegetation exerts several effects that heighten the quality of life (alleviation of unpleasant weather conditions, mitigation of erosion, aesthetics, among others). For this reason, there is an increased interest in the development of automated tools for studying the temporal and spatial evolution of the vegetation cover in wide urban areas, with an adequate spatial and temporal resolution. We present an automated image processing workflow for computing the variation of vegetation cover using any publicly available satellite imagery (ASTER, SPOT, LANDSAT, MODIS, among others) and a set of image processing algorithms specifically developed. The automatic processing methodology was developed to evaluate the spatial and temporal evolution of vegetation cover, including the Normalized Difference Vegetation Index (NDVI), the vegetation cover percentage and the vegetation variation. A prior urban area digitalization is required. The methodology was applied in Monte Hermoso city, Argentina. The vegetation cover per city block was computed and three transects over the city were outlined to evaluate the changes in NDVI values. This allows the computation of several information products, like NDVI profiles, vegetation variation assessment, and classification of city areas regarding vegetation. The information is available in GIS-readable formats, making it useful as support for urban planning decisions.A vegetação tem um papel importante como indicador de efeitos antrópicos, especificamente nos casos em que o planejamento urbano é necessário. Este é especialmente o caso na gestão de cidades costeiras, onde a vegetação exerce diversos efeitos que elevam a qualidade de vida (alívio de condições climáticas desagradáveis, mitigação da erosão, estética, entre outras). Por essa razão, há um interesse crescente no desenvolvimento de ferramentas automatizadas para o estudo da evolução temporal e espacial da cobertura vegetal em grandes áreas urbanas, com adequada resolução espacial e temporal. Apresentamos um fluxo de trabalho automatizado de processamento de imagens para calcular a variação da cobertura vegetal usando qualquer imagem de satélite publicamente disponível (ASTER, SPOT, LANDSAT, MODIS, entre outros) e um conjunto de algoritmos de processamento de imagem desenvolvidos especificamente. A metodologia de processamento automático foi desenvolvida para avaliar a evolução espacial e temporal da cobertura vegetal, incluindo o Índice de Vegetação da Diferença Normalizada (NDVI), o percentual de cobertura vegetal e a variação da vegetação. Uma digitalização prévia da área urbana foi necessária. A metodologia foi aplicada na cidade de Monte Hermoso, na Argentina. A cobertura vegetal por quarteirão foi computada e três transectos sobre a cidade foram delineados para avaliar as mudanças nos valores de NDVI. Isso permite o cálculo de vários produtos de informação, como perfis de NDVI, avaliação da variação da vegetação e classificação das áreas da cidade em relação à vegetação. A informação está disponível em formatos legíveis pelo GIS, tornando-a útil como suporte para decisões de planejamento urbano.Fil: Revollo Sarmiento, Natalia Veronica. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Revollo Sarmiento, Gisela Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Huamantinco Cisneros, María Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; ArgentinaFil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Piccolo, Maria Cintia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; Argentin

TV White Spaces: A Pragmatic Approach

190 pages The editors and publisher have taken due care in preparation of this book, but make no expressed or implied warranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information contained herein. Links to websites imply neither responsibility for, nor approval of, the information contained in those other web sites on the part of ICTP. No intellectual property rights are transferred to ICTP via this book, and the authors/readers will be free to use the given material for educational purposes.  e ICTP will not transfer rights to other organizations, nor will it be used for any commercial purposes. ICTP is not to endorse or sponsor any particular commercial product, service or activity mentioned in this book. This book is released under the Attribution-NonCommercial-NoDerivatives ¦.þ International license. For more details regarding your rights to use and redistribute this work, see http://creativecommons.org/licenses/by-nc-nd/4.0/

Linking thermal variability and change to urban growth in Harare Metropolitan City using remotely sensed data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Urban growth, which involves Land Use and Land Cover Changes (LULCC), alters land surface thermal properties. Within the framework of rapid urban growth and global warming, land surface temperature (LST) and its elevation have potential significant socio-economic and environmental implications. Hence the main objectives of this study were to (i) map urban growth, (ii) link urban growth with indoor and outdoor thermal conditions and (iii) estimate implications of thermal trends on household energy consumption as well as predict future urban growth and temperature patterns in Harare Metropolitan, Zimbabwe. To achieve these objectives, broadband multi-spectral Landsat 5, 7 and 8, in-situ LULC observations, air temperature (Ta) and humidity data were integrated. LULC maps were obtained from multi-spectral remote sensing data and derived indices using the Support Vector Machine Algorithm, while LST were derived by applying single channel and split window algorithms. To improve remote sensing based urban growth mapping, a method of combining multi-spectral reflective data with thermal data and vegetation indices was tested. Vegetation indices were also combined with socio-demographic data to map the spatial distribution of heat vulnerability in Harare. Changes in outdoor human thermal discomfort in response to seasonal LULCC were evaluated, using the Discomfort Index (DI) derived parsimoniously from LST retrieved from Landsat 8 data. Responses of LST to long term urban growth were analysed for the period from 1984 to 2015. The implications of urban growth induced temperature changes on household air-conditioning energy demand were analysed using Landsat derived land surface temperature based Degree Days. Finally, the Cellular Automata Markov Chain (CAMC) analysis was used to predict future landscape transformation at 10-year time steps from 2015 to 2045. Results showed high overall accuracy of 89.33% and kappa index above 0.86 obtained, using Landsat 8 bands and indices. Similar results were observed when indices were used as stand-alone dataset (above 80%). Landsat 8 derived bio-physical surface properties and socio-demographic factors, showed that heat vulnerability was high in over 40% in densely built-up areas with low-income when compared to “leafy” suburbs. A strong spatial correlation (α = 0.61) between heat vulnerability and surface temperatures in the hot season was obtained, implying that LST is a good indicator of heat vulnerability in the area. LST based discomfort assessment approach retrieved DI with high accuracy as indicated by mean percentage error of less than 20% for each sub-season. Outdoor thermal discomfort was high in hot dry season (mean DI of 31oC), while the post rainy season was the most comfortable (mean DI of 19.9oC). During the hot season, thermal discomfort was very low in low density residential areas, which are characterised by forests and well maintained parks (DI ≤27oC). Long term changes results showed that high density residential areas increased by 92% between 1984 and 2016 at the expense of cooler green-spaces, which decreased by 75.5%, translating to a 1.98oC mean surface temperature increase. Due to surface alterations from urban growth between 1984 and 2015, LST increased by an average of 2.26oC and 4.10oC in the cool and hot season, respectively. This decreased potential indoor heating energy needed in the cool season by 1 degree day and increased indoor cooling energy during the hot season by 3 degree days. Spatial analysis showed that during the hot season, actual energy consumption was low in high temperature zones. This coincided with areas occupied by low income strata indicating that they do not afford as much energy and air conditioning facilities as expected. Besides quantifying and strongly relating with energy requirement, degree days provided a quantitative measure of heat vulnerability in Harare. Testing vegetation indices for predictive power showed that the Urban Index (UI) was comparatively the best predictor of future urban surface temperature (r = 0.98). The mean absolute percentage error of the UI derived temperature was 5.27% when tested against temperature derived from thermal band in October 2015. Using UI as predictor variable in CAMC analysis, we predicted that the low surface temperature class (18-28oC) will decrease in coverage, while the high temperature category (36-45oC) will increase in proportion covered from 42.5 to 58% of city, indicating further warming as the city continues to grow between 2015 and 2040. Overall, the findings of this study showed that LST, human thermal comfort and air-conditioning energy demand are strongly affected by seasonal and urban growth induced land cover changes. It can be observed that urban greenery and wetlands play a significant role of reducing LST and heat transfer between the surface and lower atmosphere and LST may continue unless effective mitigation strategies, such as effective vegetation cover spatial configuration are adopted. Limitations to the study included inadequate spatial and low temporal resolution of Landsat data, few in-situ observations of temperature and LULC classification which was area specific thus difficult for global comparison. Recommendations for future studies included data merging to improve spatial and temporal representation of remote sensing data, resource mobilization to increase urban weather station density and image classification into local climate zones which are of easy global interpretation and comparison

Analysis of vegetation fragmentation and impacts using remote sensing techniques in the Eastern Arc Mountains of Tanzania.

Ph. D. University of KwaZulu-Natal, Pietermaritzburg 2014.The Eastern Arc Mountains of Tanzania forms part of the Eastern Afromontane Biodiversity Hotspots, listed among the global World Wide Fund for Nature's (WWF) priority ecoregions. However, the region is threatened by fragmentation and habitat modification resulting from competing forms of land uses, which is in turn threatening biodiversity conservation, planning and management efforts. To determine vulnerability that can inform long-term conservation and management of the biodiversity hotspots, an in-depth understanding of the qualitative and quantitative nature of ecosystems is a pre-requisite. The overall goal of this study was to quantify fragmentation, investigate its impacts on tree species diversity, abundance and biomass and to identify management interventions in the Eastern Arc Mountains of Tanzania. Using ecological field based measurements and a series of LANDSAT and RapidEye satellite imagery, fragstats metrics showed dynamic fragmentation patterns at both spatial and temporal scales. Furthermore, species diversity was predicted better with customized environmental variables using the Generic Algorithm for Rule-Set Prediction (GARP) model, which recorded an Area under Curve (AUC) of 0.89. In addition, Poisson regression results showed different responses by individual tree species to patch area dynamics, habitat status and soil nitrogen. Partial Least Squares and Random Forest models were used to determine above ground biomass prediction based on a combination of edaphic variables and vegetation indices. Total biomass estimations decreased from 1162 ton ha-1 in 1980 to 285.38 ton ha-1 in 2012. As a reference point in formulation of policy insights based on strong scientific and empirical knowledge, socio-economic factors influencing vulnerability of ecosystems and management interventions were examined using remotely sensed and empirical data from 335 households. The multiple logistic regression model indicated habitat fragmentation and forest burning as key conservation threats while low income level (54.62%) and limited knowledge on environmental conservation (18.51%) were identified as major catalysts to ecosystem vulnerability. The study identified livelihood diversification, effective institutional frameworks and afforestation programmes as major intervention measures. The overall study shows the effectiveness of remote sensing techniques in ecological studies and how results can be used to inform decisions for addressing complex ecological challenges in the tropics

Mapping and Assessing Impacts of Land Use and Land Cover Change by Means of Advanced Remote Sensing Approach:: Mapping and Assessing Impacts of Land Use and Land Cover Change by Means of Advanced Remote Sensing Approach:: A case Study of Gash Agricultural Scheme, Eastern Sudan

Risks and uncertainties are unavoidable in agriculture in Sudan, due to its dependence on climatic factors and to the imperfect nature of the agricultural decisions and policies attributed to land cover and land use changes that occur. The current study was conducted in the Gash Agricultural Scheme (GAS) - Kassala State, as a semi-arid land in eastern Sudan. The scheme has been established to contribute to the rural development, to help stability of the nomadic population in eastern Sudan, particularly the local population around the Gash river areas, and to facilitate utilizing the river flood in growing cotton and other cash crops. In the last decade, the scheme production has declined, because of drought periods, which hit the region, sand invasion and the spread of invasive mesquite trees, in addition to administrative negligence. These have resulted also in poor agricultural productivity and the displacement of farmers away from the scheme area. Recently, the scheme is heavily disturbed by human intervention in many aspects. Consequently, resources of cultivated land have shrunk and declined during the period of the study, which in turn have led to dissatisfaction and increasing failure of satisfying increasing farmer’s income and demand for local consumption. Remote sensing applications and geospatial techniques have played a key role in studying different types of hazards whether they are natural or manmade. Multi-temporal satellite data combined with ancillary data were used to monitor, analyze and to assess land use and land cover (LULC) changes and the impact of land degradation on the scheme production, which provides the managers and decision makers with current and improved data for the purposes of proper administration of natural resources in the GAS. Information about patterns of LULC changes through time in the GAS is not only important for the management and planning, but also for a better understanding of human dimensions of environmental changes at regional scale. This study attempts to map and assess the impacts of LULC change and land degradation in GAS during a period of 38 years from 1972-2010. Dry season multi-temporal satellite imagery collected by different sensor systems was selected such as three cloud-free Landsat (MSS 1972, TM 1987 and ETM+ 1999) and ASTER (2010) satellite imagery. This imagery was geo-referenced and radiometrically and atmospherically calibrated using dark object subtraction (DOS). Two approaches of classification (object-oriented and pixel-based) were applied for classification and comparison of LULC. In addition, the study compares between the two approaches to determine which one is more compatible for classification of LULC of the GAS. The pixel-based approach performed slightly better than the object-oriented approach in the classification of LULC in the study area. Application of multi-temporal remote sensing data proved to be successful for the identification and mapping of LULC into five main classes as follows: woodland dominated by dense mesquite trees, grass and shrubs dominated by less dense mesquite trees, bare and cultivated land, stabilized fine sand and mobile sand. After image enhancement successful classification of imagery was achieved using pixel and object based approaches as well as subsequent change detection (image differencing and change matrix), supported by classification accuracy assessments and post-classification. Comparison of LULC changes shows that the land cover of GAS has changed dramatically during the investigated period. It has been discovered that more significant of LULC change processes occurred during the second studied period (1987 to 1999) than during the first period (1972-1987). In the second period nearly half of bare and cultivated lands was changed from 41372.74 ha (20.22 %) in 1987 to 28020.80 ha (13.60 %) in 1999, which was mainly due to the drought that hit the region during the mentioned period. However, the results revealed a drastic loss of bare and cultivated land, equivalent to more than 40% during the entire period (1972-2010). Throughout the whole period of study, drought and invasion of both mesquite trees and sand were responsible for the loss of more than 40% of the total productive lands. Change vector analysis (CVA) as a useful approach was applied for estimating change detection in both magnitude and direction of change. The promising approach of multivariate alteration detection (MAD) and subsequent maximum autocorrelation factor (MAD/MAF) transformation was used to support change detection via assessment of maximum correlation between the transformed variates and the specific original image bands related to specific land cover classes. However, both CVA and MAD/MAD strongly prove the fact that bare and cultivated land have dramatically changed and decreased continuously during the studied period. Both CVA and MAD/MAD demonstrate adequate potentials for monitoring, detecting, identifying and mapping the changes. Moreover, this research demonstrated that CVA and MAD/MAF are superior in providing qualitative details about the nature of all kinds of change. Vegetation indices (VI) such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified adjusted vegetation index (MSAVI) and grain soil index (GSI) were applied to measure the quantitative characterization of temporal and spatial vegetation cover patterns and change. All indices remain very sensitive to structure variation of LULC. The results reveal that the NDVI is more effective for detecting the amount and status of the vegetation cover in the study area than SAVI, MSAVI and GSI. Therefore, it can be stated that NDVI can be used as a response variable to identify drought disturbance and land degradation in semi-arid land such as the GAS area. Results of detecting vegetation cover observed by using SAVI were found to be more reasonable than using MSAVI, although MSAVI reduces the background of bare soil better than SAVI. GSI proves high efficiency in determining the different types of surface soils, and producing a change map of top soil grain size, which is useful in assessment of land degradation in the study area. The linkage between socio-economic data and remotely sensed data was applied to determine the relationships between the different factors derived and to analyze the reasons for change in LULC and land degradation and its effects in the study area. The results indicate a strong relationship between LULC derived from remotely sensed data and the influencing socioeconomic variables. The results obtained from analyzing socioeconomic data confirm the findings of remote sensing data analysis, which assure that the decline and degradation of agricultural land is a result of further spread of mesquite trees and of increased invasion of sand during the study period. High livestock density and overgrazing, drought, invasion of sand, spread of invasive mesquite trees, overexploitation of land, improper management, and population growth were considered as the main direct factors responsible for degradation in the study area

The politics of internet infrastructure: communication policy, governmentality and subjectivation in Chhattisgarh, India

This thesis examines the role of internet infrastructure and its associated discourses in processes of governmentality and subject formation in low and middle-income countries of the global South. Using data collected in the central Indian state of Chhattisgarh as its core case, it asks questions about the interrelationship between policy and political discourse, new information and communications infrastructure, private capital, and how citizens come to know and/or experience internet infrastructure in their everyday lives. Since 2014, The National Optical Fibre Network (NOFN, which began in 2011 under the then UPA government) has been reshaped and rebranded as part of ‘Digital India’ by Prime Minister Narendra Modi and his far-right Hindutva Bharatiya Janata Party (BJP). Alongside the cables and connectivity, the BJP and allied Hindu extremist organisations have targeted minorities and women through mob violence and (on and offline) hate speech, while a small number of crony capitalist corporations have seen immense profits. Unpicking the links between these processes, the thesis argues that internet infrastructure has become crucial to expanding a particular unregulated brand of capitalism and to narrowing civic subjectivities. Infrastructures constellate and circulate material and symbolic goods in an institutionalised manner to produce collectivities. Using discourse analysis shows that since the late 1980s, in a context of increasing neoliberalism, internet infrastructure emerged within a discursive regime marked by the fetishisation of systems rationality, enumeration, scientism and economism to produce what can be called digital governmentality. Digital governmentality enables and reinforces a centralised Hindu nationalism mediated by digital technologies and networks. Using semi-structured interviews and participant observation in the city of Ambikapur, and close to 50 villages in Surguja district of Chhattisgarh, data chapters describe a wide range of ‘infrastructural practices.’ The analysis centres on how subjects imagine, frame and experience these practices. Dominant caste groups in Ambikapur seek to subvert governmentality in practice but also uphold and reproduce the rationalities that drive governmental authority – such as efficiency and transparency. Adivasis (indigenous groups) who reside in surrounding rural areas are subject to a political economic regime overdetermined by coal mining and destruction of their land, forests and water resources. Internet infrastructure is non-existent or broken, along with other missing infrastructural substrates such as electricity and water. Adivasis face infrastructural control as a specific mode of governmentality where power is exerted not from the top in directly coercive ways but rather through mundane infrastructural practices, thereby exerting authority in procedural ways. In other instances, Adivasis’ processes of subject formation are entangled with (the reality and promise of) internet infrastructure in complex ways – ranging from cruel optimism to social haunting. The thesis makes an original contribution to the emerging sub-field of infrastructure studies by providing a new way of studying communicative infrastructures involving: a renewed emphasis on relationality (infrastructures, governmentality and subjectivation as relational processes and practices); situating internet infrastructures within broader infrastructures; and a historical analysis of how infrastructure is caught up in exercise of power relations. With significant emphasis on the concerns and interests of indigenous peoples in India, the final chapters of the thesis also contribute to a decolonisation of media and communications as a field, and to avoiding orientalist essentialism

Horticultural Crop Response to Different Environmental and Nutritional Stress

Environmental conditions and nutritional stress may greatly affect crop performance. Abiotic stresses such as temperature (cold, heat), water (drought, flooding), irradiance, salinity, nutrients, and heavy metals can strongly affect plant growth dynamics and the yield and quality of horticultural products. Such effects have become of greater importance during the course of global climate change. Different strategies and techniques can be used to detect, investigate, and mitigate the effects of environmental and nutritional stress. Horticultural crop management is moving towards digitized, precision management through wireless remote-control solutions, but data analysis, although a traditional approach, remains the basis of stress detection and crop management. This Special Issue summarizes the recent progress in agronomic management strategies to detect and reduce environmental and nutritional stress effects on the yield and quality of horticultural crops

2019 Oklahoma Research Day Full Program

Oklahoma Research Day 2019 - SWOSU Celebrating 20 years of Undergraduate Research Successes