MONITORING VEGETATION DENSITY USING SPECTRAL VEGETATION INDICES: A CASE STUDY OF MALAM JABBA REGION, DISTRICT SWAT, PAKISTAN

The limited forest resources with a higher deforestation rate per annum, Pakistan ranks the second highest in Asia. FAO reported that the annual forest cover change rate during 1990–2000 was −1.8% and increased to −2.2 % between 2000–2010. Most of Pakistan's total forest resources, dominantly natural forest, are situated in the Northern regions. Stepping into the corridor of the 21st century, the Spatio-temporal analysis has been evolved using Satellite Remote Sensing data aided with Geographic Information System) GIS) platforms. The study is carried out over the mountainous vegetation land of Malam Jabba, district Swat, Khyber Pakhtunkhwa, Pakistan. Due to varying topography and the region being part of the agro-forestry zone, drastic changes were observed in vegetation and built-up areas. The vegetation cover has been identified and classified based on elevation throughout the area. This study has provided essential insights into vegetation cover change over a period of four decades. Vegetation cover is classified into high to very high, medium, and low to very low. The Landsat and the SRTM DEM satellite imageries were exported to the ERDAS software for pre-and post-processing, and for further analysis ArcGIS 10.5 was used, where the vegetation density change for each period was computed from the pixels by using vegetation indices like VCI, NDVI, and SAVI. The results show a significant decline from 1980 to 2010 in vegetation density in the Northwestern direction; however, an increasing trend can be seen in 2020 due to awareness and the Government’s Billion Tree Tsunami initiative. Such studies can significantly benefit researchers and decision-makers interested in satellite remote sensing for forest and other vegetation cover monitoring and management at a regional scale

Characterizing station aridity and improving the estimates of reference evapotranspiration in the Oklahoma Mesonet

There is a consensus among the scientific community regarding the rise in air temperatures and changing precipitation patterns across the globe. Many areas around the world are expected to see increased aridity levels in the future. The trends will likely impact the agricultural water availability, especially in water-scarce regions. As freshwater water availability declines in water-scarce agricultural regions, it is important for the producers to use it efficiently. Therefore, the objectives of this dissertation are: (1) To analyze the historical trends in temperature, rainfall, and reference evapotranspiration on a climate divisional scale across Oklahoma using the available datasets to provide insights about the implications of these trends on agricultural water management; (2) To examine station aridity in the Oklahoma Mesonet stations to investigate its prevalence and spatiotemporal patterns; and (3) To demonstrate the implications of station aridity for reference evapotranspiration and improve the estimation of the reference evapotranspiration in the Oklahoma Mesonet stations to facilitate potential irrigation water savings in the State of Oklahoma. The results reveal increasing air temperature and precipitation trends on annual and seasonal scales and decreasing reference evapotranspiration trends in summer in Oklahoma which are consistent with the findings of other researchers in the Great Plains region. Station aridity is prevalent in the dry western part of the state which hinders the Mesonet’s ability to provide accurate data on reference evapotranspiration. Station aridity effects are more pronounced during droughts, limiting the utility of the estimated reference evapotranspiration in areas and at times that accurate information is critically needed to support agricultural water conservation. It is demonstrated that air temperature and humidity datasets can be adjusted to improve the reference evapotranspiration estimates using the available and a newly developed methodology using NDVI

Performance Evaluation of Job Scheduling and Resource Allocation in Apache Spark

Advancements in data acquisition techniques and devices are revolutionizing the way image data are collected, managed and processed. Devices such as time-lapse cameras and multispectral cameras generate large amount of image data daily. Therefore, there is a clear need for many organizations and researchers to deal with large volume of image data efficiently. On the other hand, Big Data processing on distributed systems such as Apache Spark are gaining popularity in recent years. Apache Spark is a widely used in-memory framework for distributed processing of large datasets on a cluster of inexpensive computers. This thesis proposes using Spark for distributed processing of large amount of image data in a time efficient manner. However, to share cluster resources efficiently, multiple image processing applications submitted to the cluster must be appropriately scheduled by Spark cluster managers to take advantage of all the compute power and storage capacity of the cluster. Spark can run on three cluster managers including Standalone, Mesos and YARN, and provides several configuration parameters that control how resources are allocated and scheduled. Using default settings for these multiple parameters is not enough to efficiently share cluster resources between multiple applications running concurrently. This leads to performance issues and resource underutilization because cluster administrators and users do not know which Spark cluster manager is the right fit for their applications and how the scheduling behaviour and parameter settings of these cluster managers affect the performance of their applications in terms of resource utilization and response times. This thesis parallelized a set of heterogeneous image processing applications including Image Registration, Flower Counter and Image Clustering, and presents extensive comparisons and analyses of running these applications on a large server and a Spark cluster using three different cluster managers for resource allocation, including Standalone, Apache Mesos and Hodoop YARN. In addition, the thesis examined the two different job scheduling and resource allocations modes available in Spark: static and dynamic allocation. Furthermore, the thesis explored the various configurations available on both modes that control speculative execution of tasks, resource size and the number of parallel tasks per job, and explained their impact on image processing applications. The thesis aims to show that using optimal values for these parameters reduces jobs makespan, maximizes cluster utilization, and ensures each application is allocated a fair share of cluster resources in a timely manner

Emprego de séries temporais na Amazônia : análise de imagens MODIS e RADAR para mapeamento de uso e ocupação do solo no Estado do Acre

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2019.Objetivou-se avaliar metodologias para trabalhar séries temporais na Amazônia, em ambiente com alta frequência de nuvens. A região amazônica abriga grande biodiversidade ambiental e diversidade sociocultural, justificando a necessidade de estudos para acompanhar a dinâmica de uso e ocupação do solo, bem como realizar o monitoramento dos recursos ambientais. Para tanto, foram desenvolvidos 3 capítulos em formato de artigos. No primeiro artigo, avaliou-se a quantidade ideal de imagens para uma série temporal visando melhores resultados de classificação de uso e ocupação do solo. Observou-se que os cubos temporais precisam ter imagens claras para atingir os melhores resultados das classificações. A presença de imagens com nuvem resulta em classificações com baixo índice kappa. Dentre os classificadores utilizados, o Mínima Distância foi o que se apresentou menos sensível à presença de nuvens. No segundo artigo, foram avaliadas técnicas de composição afim de encontrar uma síntese que abrangesse os períodos secos e chuvosos na região. Observou-se que intervalos regulares de composição não foram suficientes para obtenção de imagens livres de nuvem ao longo do ano. Intervalos irregulares (personalizados) de composição podem trazer um maior número de dados ao pesquisador, sobretudo, em ambientes com alta frequência de cobertura de nuvem. Dentre os métodos síntese testados, o de Máximo NDVI e o de Mediana apresentaram os melhores resultados. A restrição do ângulo do sensor zênite levou a composições mais limpas, ou seja, menos influenciadas por fatores geométricos e atmosféricos. No terceiro artigo, utilizamos uma série temporal de imagens de radar, avaliando sua capacidade de identificar alvos como o cultivo da cana-de-açúcar. Dentre os testes realizados, aquele que combinou dados de radar e óticos na série temporal, apresentou os melhores resultados. Foi possível identificar a cana-de-açúcar, com mais de 50% de acertos na maioria dos testes realizados. Concluímos que as análises com dados orbitais para a classificação de séries temporais na Amazônia, são possíveis, embora as séries temporais necessitem ter baixa influência de cobertura de nuvem. As possibilidades que se apresentam com as imagens de radar Sentinel são grandes considerando o volume de dados disponíveis, com boa resolução temporal e espacial.This study aimed to evaluate methodologies for working time series in the Amazon, in a high cloud frequency environment. The Amazon region is home to great environmental biodiversity and sociocultural diversity, justifying the need for studies to follow the dynamics of land use and occupation, as well as monitoring environmental resources. For that, 3 chapters were developed in paper format. In the first paper, the ideal amount of images for a temporal series was evaluated aiming at better results of classification of use and occupation of the soil. It was observed that temporal cubes need to have clear images to achieve the best results of the classifications. The presence of cloud images results in ratings with a low kappa index. Among the classifiers used, the Minimum Distance was the one that was less sensitive to the presence of clouds in the time series. In the second paper, image composition techniques were evaluated in order to find a synthesis that covered the dry and rainy periods in the region. It was observed that regular composition intervals were not enough to obtain cloud-free images throughout the year. Irregular (custom) composition intervals can bring a greater amount of data to the researcher, especially in environments with high frequency cloud coverage. Among the synthesis methods tested, Maximum NDVI and Median presented the best results. Restriction of the zenith sensor angle has led to cleaner compositions that is lessinfluenced by geometric and atmospheric factors. In the third article, we used a time series of radar images, evaluating their ability to identify targets such as sugarcane cultivation. Among the tests performed, the one that combined radar and optical data in the time series presented the best results. It was possible to identify the sugarcane, with more than 50% of correct answers in most of the tests performed. We conclude that the analyzes with orbital data for the classification of time series in the Amazon, are possible, although the time series need to have low influence of cloud coverage. The possibilities presented with Sentinel radar images are large considering the volume of data available, with good temporal and spatial resolution

The effect of fire disturbances on woody plant encroachment at Loskop, Irene and Roodeplaat Farms, South Africa

Dissertation (MSc (Environmental Management))--University of Pretoria, 2022.The study on bush encroachment goes as far back as 1917, which is ranked among the top three rangeland problems in South Africa with expected increase in affected areas. Bush encroachment is considered one of the most substantial forms of land degradation because it occurs at the expense of beneficial herbaceous layer. Even with substantial number of studies on bush encroachment, the studies have not provided a broad comprehension of the problem, which complicates its management. Climate change, fire regimes herbivory and excessive increase of CO2 in the atmosphere are some of the key drivers of the current levels of bush encroachment. It is estimated that 20 million ha of South Africa’s agricultural productivity and biodiversity is under the threat of bush encroachment. As a result, the economic productivity of affected rangelands is negatively affected. This study investigated the effects of fire frequency/history (the rate of fire occurrence over an area in a given time period) on tree density and plant diversity. It further investigates the contribution of fire to the current extent of bush encroachment using remote sensing data over a nineteen year-period from the year 2000 to 2019. The study sites are based on three Agricultural Research Council (ARC) farms namely Loskop, Irene and Roodeplaat. Firstly, the in-situ and remotely sensed moderate resolution imaging spectroradiometer (MODIS) data were used to determine how fire influences the vegetation structure (tree density and plant diversity) using Analysis of Variance (ANOVA and Kruskal-Wallis (KW-H)). Secondly, in-situ, MODIS and Landsat data were used to build models needed for mapping areas of tree density change. The study investigated the indicators of bush encroachment namely, tree density within the study sites. The study found that there is a low to moderate correlation between burned areas and tree density in Loskop, Irene and Roodeplaat farms with the Pearson correlation coefficients of -0.06, 0.38 and 0.38 respectively. The significant tree density models had moderate to relatively high R-squares of 0.59, 0.49 and 0.82 for Loskop, Irene and Roodeplaat farms respectively. The findings of this study showed that fire frequency did not significantly influence the bush encroachment as measured by tree density and diversity in Loskop and Roodeplaat farms. However, there was evidence of fire frequency significantly influencing an increase in tree density in Irene farm. Due to lack of herbivores in some parts of Loskop and Roodeplaat farms because of water scarcity, fire alone may have not been a frequent enough disturbance to significantly influence tree density. The models calculated in this study serve as a foundation for understanding and calculating the tree density in response to fire. The findings of this study serve as a guide for resource managers to better manage fire regimes and their effect on vegetation cover at a local scale. Keywords: Fire, Plant diversity, Remote sensing, Tree densityAgricultural Research Council (ARC)Geography, Geoinformatics and MeteorologyMSc (Environmental Management)Unrestricte