Land Cover Change Image Analysis for Assateague Island National Seashore Following Hurricane Sandy

The assessment of storm damages is critically important if resource managers are to understand the impacts of weather pattern changes and sea level rise on their lands and develop management strategies to mitigate its effects. This study was performed to detect land cover change on Assateague Island as a result of Hurricane Sandy. Several single-date classifications were performed on the pre and post hurricane imagery utilized using both a pixel-based and object-based approach with the Random Forest classifier. Univariate image differencing and a post classification comparison were used to conduct the change detection. This study found that the addition of the coastal blue band to the Landsat 8 sensor did not improve classification accuracy and there was also no statistically significant improvement in classification accuracy using Landsat 8 compared to Landsat 5. Furthermore, there was no significant difference found between object-based and pixel-based classification. Change totals were estimated on Assateague Island following Hurricane Sandy and were found to be minimal, occurring predominately in the most active sections of the island in terms of land cover change, however, the post classification detected significantly more change, mainly due to classification errors in the single-date maps used

Incorporating plant community structure in species distribution modelling: a species co-occurrence based composite approach

Species distribution models (SDM) with remotely sensed (RS) imagery is widely used in ecological studies and conservation planning, and the performance is frequently limited by factors including small plant size, small numbers of observations, and scattered distribution patterns. The focus of my thesis was to develop and evaluate alternative SDM methodologies to deal with such challenges. I used a record of nine endemic species occurrences from the Athabasca Sand Dunes in northern Saskatchewan to assess five different modelling algorithms including modern regression and machine learning techniques to understand how species distribution characteristics influence model prediction accuracies. All modelling algorithms showed robust performance (>0.5 AUC), with the best performance in most cases from generalized linear models (GLM). The threshold selection for presence-absence analysis highlights that actively selecting the optimum level is the best approach compared to the standard high threshold approach as with the latter there is a potential to deliver inconsistent predictions compared to observed patterns of occurrence frequency. The development of the composite-SDM framework used small-scale plant occurrence and UAV imagery from Kernen Prairie, a remnant Fescue prairie in Saskatoon, Saskatchewan. The evaluation of the effectiveness of five algorithms clearly showed that each method was capable of handling a wide range of low to high-frequency species with strong GLM performance irrespective of the species distribution pattern. It is critical to highlight that, although GLM is computationally efficient, the method does not compromise accuracy for simplicity. The inclusion of plant community structure using image clustering methods found similar accuracy patterns indicating limited advantages of using high-resolution images. The study found for high-frequency species that prediction accuracy declines to be as low as the accuracy expected for low-frequency species. Higher prediction confidence was often observed with low-frequency species when the species occurred in a distinct habitat that was visually and spectrally distinct from the surroundings. Such a pattern is in contrast to species widespread in different grassland habitats where distinct spectral signatures were lacking. The study has substantial evidence to state that the optimal algorithmic performance is tied to a balanced number of presences and absences in the data. The co-occurrence analysis also revealed significant co-occurrence patterns are most common at moderate levels of species occurrence frequencies. The research does not indicate any consistent accuracy changes between baseline direct reflectance models and composite-SDM framework. Although accuracy changes were marginal with the composite-SDM framework, the method is well capable of influencing associated type 1 and type 2 error rates of the classification

Incorporating plant community structure in species distribution modelling: a species co-occurrence based composite approach

Species distribution models (SDM) with remotely sensed (RS) imagery is widely used in ecological studies and conservation planning, and the performance is frequently limited by factors including small plant size, small numbers of observations, and scattered distribution patterns. The focus of my thesis was to develop and evaluate alternative SDM methodologies to deal with such challenges. I used a record of nine endemic species occurrences from the Athabasca Sand Dunes in northern Saskatchewan to assess five different modelling algorithms including modern regression and machine learning techniques to understand how species distribution characteristics influence model prediction accuracies. All modelling algorithms showed robust performance (>0.5 AUC), with the best performance in most cases from generalized linear models (GLM). The threshold selection for presence-absence analysis highlights that actively selecting the optimum level is the best approach compared to the standard high threshold approach as with the latter there is a potential to deliver inconsistent predictions compared to observed patterns of occurrence frequency. The development of the composite-SDM framework used small-scale plant occurrence and UAV imagery from Kernen Prairie, a remnant Fescue prairie in Saskatoon, Saskatchewan. The evaluation of the effectiveness of five algorithms clearly showed that each method was capable of handling a wide range of low to high-frequency species with strong GLM performance irrespective of the species distribution pattern. It is critical to highlight that, although GLM is computationally efficient, the method does not compromise accuracy for simplicity. The inclusion of plant community structure using image clustering methods found similar accuracy patterns indicating limited advantages of using high-resolution images. The study found for high-frequency species that prediction accuracy declines to be as low as the accuracy expected for low-frequency species. Higher prediction confidence was often observed with low-frequency species when the species occurred in a distinct habitat that was visually and spectrally distinct from the surroundings. Such a pattern is in contrast to species widespread in different grassland habitats where distinct spectral signatures were lacking. The study has substantial evidence to state that the optimal algorithmic performance is tied to a balanced number of presences and absences in the data. The co-occurrence analysis also revealed significant co-occurrence patterns are most common at moderate levels of species occurrence frequencies. The research does not indicate any consistent accuracy changes between baseline direct reflectance models and composite-SDM framework. Although accuracy changes were marginal with the composite-SDM framework, the method is well capable of influencing associated type 1 and type 2 error rates of the classification

Estimating the Onset and Extent of Dieback Of Phragmites australis Using the Normalized Difference Vegetation Index and Remotely Sensed Land Cover Classifications

Phragmites australis is cosmopolitan plant species with an invasive variety present throughout most of North America. In the Balize Delta, Louisiana, USA, P. australis plays an important role in combatting subsidence, maintaining navigation channels, and protecting interior fish and wildlife habitat from waves and storm surge. In 2016 a dieback of P. australis was reported by wetland managers, coinciding with the appearance of an invasive Asian scale insect (Nipponaclerda biwakoensis), though the specific cause is still unknown. Two previous efforts attempted to identify the onset of dieback conditions met with limited success. Using Landsat images from 1985 to 2019 we classified P. australis. That classification was ground-truthed with information from five helicopter surveys made between 1988 and 2013. P. australis was stable from 2010 until 2014 but then decreased in area in 2015 and decreased in NDVI from 2014 to 2016. Area of total marsh vegetation and P. australis varied in similar patterns from the 1980s until the 2000s; since then, they vary in different ways. I concluded that detectable dieback conditions in the area began as early as 2014 and started recovery post 2016. Spatial patterns of decline is consistent with multiple stressors inducing dieback conditions such as eutrophication, salinity, or water level

A remote sensing approach to the quantification of local to global scale social-ecological impacts of anthropogenic landscape changes

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsLanduse and Landcover (LULC) is the common aspect that influences several ecological issues, environmental degradations, changes in Land Surface Temperature (LST), hydrological changes and ecosystem function at regional to global level. Research on the drivers and progressions of LULC change has been key to developing models that can project and predict future LULC extent, level and patterns under different assumptions of socioeconomic, ecological and environmental situations. Rapid and extensive urbanization and Urban Sprawl (US), propelled by rapid population growth leads to the shrinkage of productive agricultural lands, boosting mining, decrease in surface permeability and the emergence of Urban Heat Islands (UHI), and in turn, adversely affects the provision of ecosystem services. Mining for resources extraction may lead to geological and associated environmental changes due to ground movements, collision with mining cavities, and deformation of aquifers. Geological changes may continue in a reclaimed mine area, and the deformed aquifers may entail a breakdown of substrates and an increase in ground water tables, which may cause surface area inundation. Consequently, a reclaimed mine area may experience surface area collapse, i.e., subsidence, and degradation of vegetation productivity. The greater changes in LULC, US, LST and vegetation dynamics due to increasing human population not only affects inland forest and wetland, it also directly influences coastal forest lands such as mangroves, peat swamps and riparian forest and threats to ecosystem services. Mangroves provide valuable provisioning (e.g. aquaculture, fisheries, fuel, medicine, textiles), regulation (e.g. shoreline protection, erosion control, climate regulation), supporting (nutrient cycling, nursery habitat), and cultural (recreation and tourism) ecosystem services with an important impact on human well-being. However, the mangrove forest is highly threatened due to climate changes, and human activities which ignore the ecological and economic value of these habitats, contributing to its degradation. There is an increasing number of studies about mangrove distribution, changes and re-establishment activities, denoting a growing attentiveness on the value of these coastal wetland ecosystems. Most of these studies address mangrove degradation drivers at regional or local levels. However, there has not been yet enough assessment on the drivers of mangrove degradation at global level. Thus, complexity of inland and coastal landscape degradation should be addressed using multidisciplinary methodology and conditions. Therefore, this dissertation aimed to assess the impact of LULC associated with vegetation, temperature and wetland changes. To understand the relation among three different types of landscape changes associated with anthropogenic activities: Urbanization, Geological changes and Forest degradation at local to global level, we have selected thirty-three global regions. In chapter 2, We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed six landscape metrics to delineate the extent of urban areas within a 10km suburban buffer of Chennai city, Tamilnadu, India. The level of US was then quantified using Renyi’s entropy. A land change model was subsequently used to project land cover for 2027. A 70.35% expansion in urban areas was observed mainly towards the suburban periphery of Chennai between 1991 and 2016. The Renyi’s entropy value for year 2016 was 0.9, exhibiting a two-fold level of US when compared to 1991. The spatial metrics values indicate that the existing urban areas became denser and the suburban agricultural, forests and particularly barren lands were transformed into fragmented urban settlements. The forecasted land cover for 2027 indicates a conversion of 13,670.33 ha (16.57% of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value to 1.7, indicating a tremendous level of US. Our study provides useful metrics for urban planning authorities to address the social-ecological consequences of US and to protect ecosystem services. In chapter 3, We studied landscape dynamics in Kirchheller Heide, Germany, which experienced extensive soil movement due to longwall mining without stowing, using Landsat imageries between 2013 and 2016. A Random Forest image classification technique was applied to analyse landuse and landcover dynamics, and the growth of wetland areas was assessed using a Spectral Mixture Analysis (SMA). We also analyzed the changes in vegetation productivity using a Normalized Difference Vegetation Index (NDVI). We observed a 19.9% growth of wetland area within four years, with 87.2% growth in the coverage of two major waterbodies in the reclaimed mine area. NDVI values indicate that the productivity of 66.5% of vegetation of the Kirchheller Heide was degraded due to changes in ground water tables and surface flooding. Our results inform environmental management and mining reclamation authorities about the subsidence spots and priority mitigation areas from land surface and vegetation degradation in Kirchheller Heide. In chapter 4, We demonstrated the advantage of fusing imageries from multiple sensors for LULC change assessments as well as for assessing surface permeability and temperature and UHI emergence in a fast-growing city, i.e. Tirunelveli, Tamilnadu, India. IRS-LISSIII and Landsat-7 ETM+ imageries were fused for 2007 and 2017, and classified using a Rotation Forest (RF) algorithm. Surface permeability and temperature were then quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Finally, we assessed the relationship between SAVI and LST for entire Tirunelveli as well as for each LULC zone, and also detected UHI emergence hot spots using a SAVI-LST combined metric. Our fused images exhibited higher classification accuracies, i.e. overall kappa coefficient values, than non-fused images. We observed an overall increase in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall and also for almost all LULC zones. The LST values showed an overall increase of surface temperature in Tirunelveli with the highest increase for urban built-up areas between 2007 and 2017. LST also exhibited a strong negative association with SAVI. South-eastern built-up areas in Tirunelveli were depicted as a potential UHI hotspot, with a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability, temperature and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion. In chapter 5, We identified mangrove degradation drivers at regional and global levels resulted from decades of research data (from 1981 to present) of climate variations (seal-level rising, storms, precipitation, extremely high water events and temperature), and human activities (pollution, wood extraction, aquaculture, agriculture and urban expansion). This information can be useful for future research on mangroves, and to help delineating global planning strategies which consider the correct ecological and economic value of mangroves protecting them from further loss.O uso e a cobertura da Terra (UCT) são o aspeto comum que influencia várias questões ecológicas, degradações ambientais, mudanças na temperatura da superfície terrestre, mudanças hidrológicas, e de funções dos ecossistemas a nível regional e global. A investigação sobre os determinantes e progressão da mudança de UCT tem sido fundamental para o desenvolvimento de modelos que podem projetar e prever a extensão, o nível e os padrões futuros de UCT sob diferentes hipóteses de situações socioeconómicas, ecológicas e ambientais. A rápida e extensa urbanização e expansão urbana impulsionada pelo rápido crescimento populacional, levou ao encolhimento de terras agrícolas produtivas, impulsionando a mineração, a diminuição da permeabilidade da superfície e o surgimento de ilhas urbanas. Por outro lado, tem afetado negativamente a produção de serviços de ecossistemas. A mineração para extração de recursos pode levar a mudanças geológicas e ambientais devido a movimentos do solo, colisão com cavidades de mineração e deformação de aquíferos. As mudanças geológicas podem continuar numa área de mina recuperada, e os aquíferos deformados podem acarretar uma quebra de substratos e um aumento nos lençóis freáticos, causando a inundação na superfície. Consequentemente, uma área de mina recuperada pode sofrer um colapso à superfície, provocando o afundamento e a degradação da produtividade da vegetação. As mudanças na UCT, no crescimento urbano rápido, na temperatura da superfície terrestre e na dinâmica da vegetação devido ao aumento da população humana não afetam apenas a floresta interior e as zonas húmidas. Estas também influenciam diretamente as terras florestais costeiras, tais como mangais, pântanos e florestas ribeirinhas, ameaçando os serviços de ecossistemas. Os mangais proporcionam um aprovisionamento valioso (por exemplo, aquacultura, pesca, combustível, medicamentos, têxteis), a regulação (por exemplo, proteção da linha de costa, controlo da erosão, regulação do clima), os serviços de ecossistema de apoio (ciclo de nutrientes, habitats) e culturais (recreação e turismo) com um impacto importante no bem-estar humano. No entanto, a floresta de mangal é altamente ameaçada devido às mudanças climáticas e às atividades humanas que ignoram o valor ecológico e económico desses habitats, contribuindo para a sua degradação. Há um número crescente de estudos sobre distribuição, mudança e atividades de restabelecimento de mangais, denotando uma crescente atenção sobre o valor desses ecossistemas costeiros de zonas húmidas. A maioria desses estudos aborda os fatores de degradação dos mangais a nível regional ou local. No entanto, ainda não há avaliação suficiente sobre os determinantes da degradação dos mangais a nível global. Assim, a complexidade da degradação da paisagem interior e costeira deve ser abordada usando uma metodologia multidisciplinar. Portanto, esta dissertação teve, também, como objetivo avaliar o impacto do UCT associado à vegetação, temperatura e mudanças de zonas húmidas. Para compreender a relação entre a dinâmica da paisagem associada às atividades antrópicas a nível local e global, selecionámos quatro áreas de estudo, duas da Ásia, uma da Europa e outro estudo a nível global. No capítulo 2, empregamos a classificação Random Forest (RF) nas imagens Landsat de 1991, 2003 e 2016, e computamos seis métricas de paisagem para delinear a extensão das áreas urbanas numa área de influência suburbana de 10 km da cidade de Chennai, Tamil Nadu, Índia. O nível de crescimento urbano rápido foi quantificado usando a entropia de Renyi. Um modelo de UCT foi posteriormente usado para projetar a cobertura de terra para 2027. Uma expansão de 70,35% nas áreas urbanas foi observada principalmente para a periferia suburbana de Chennai entre 1991 e 2016. O valor de entropia do Renyi para 2016 foi de 0,9, exibindo uma duplicação do nível de crescimento urbano rápido quando comparado com 1991. Os valores das métricas espaciais indicam que as áreas urbanas existentes se tornaram mais densas e as terras agrícolas, florestas e terras particularmente áridas foram transformadas em assentamentos urbanos fragmentados. A previsão de cobertura da Terra para 2027 indica uma conversão de 13.670,33 ha (16,57% da paisagem total) de florestas e terras agrícolas existentes em áreas urbanas, com um aumento associado no valor de entropia para 1,7, indicando um tremendo nível de crescimento urbano rápido. O nosso estudo fornece métricas úteis para as autoridades de planeamento urbano para lidarem com as consequências socio-ecológicas do crescimento urbano rápido e para proteger os serviços de ecossistemas. No capítulo 3, estudamos a dinâmica da paisagem em Kirchheller Heide, Alemanha, que experimentou um movimento extensivo do solo devido à mineração, usando imagens Landsat entre 2013 e 2016. Uma técnica de classificação de imagem Random Forest foi aplicada para analisar dinâmicas de UCT e o crescimento das áreas de zonas húmidas foi avaliado usando uma Análise de Mistura Espectral. Também analisámos as mudanças na produtividade da vegetação usando um Índice de Vegetação por Diferença Normalizada (NDVI). Observámos um crescimento de 19,9% da área húmida em quatro anos, com um crescimento de 87,2% de dois principais corpos de água na área de mina recuperada. Valores de NDVI indicam que a produtividade de 66,5% da vegetação de Kirchheller Heide foi degradada devido a mudanças nos lençóis freáticos e inundações superficiais. Os resultados informam as autoridades de gestão ambiental e recuperação de mineração sobre os pontos de subsidência e áreas de mitigação prioritárias da degradação da superfície e da vegetação da terra em Kirchheller Heide. No capítulo 4, demonstramos a vantagem de fusionar imagens de múltiplos sensores para avaliações de mudanças de UCT, bem como para avaliar a permeabilidade, temperatura da superfície e a emergência do ilhas de calor numa cidade em rápido crescimento, Tirunelveli, Tamilnadu, Índia. As imagens IRS-LISSIII e Landsat-7 ETM + foram fusionadas para 2007 e 2017, e classificadas usando um algoritmo de Random Forest (RF). A permeabilidade de superfície e a temperatura foram então quantificadas usando-se o Índice de Vegetação Ajustada pelo Solo (SAVI) e o Índice de Temperatura da Superfície Terrestre (LST), respectivamente. Finalmente, avaliamos a relação entre SAVI e LST para Tirunelveli, bem como para cada zona de UCT, e também detetamos a emergência de pontos quentes de emergência usando uma métrica combinada de SAVI-LST. As nossas imagens fusionadas exibiram precisões de classificação mais altas, ou seja, valores globais do coeficiente kappa, do que as imagens não fusionadas. Observámos um aumento geral na cobertura de áreas urbanas (áreas de terrenos secos e construídas), e uma diminuição de áreas com vegetação (plantações e florestas) em Tirunelveli entre 2007 e 2017. Os valores de SAVI indicaram uma extensa diminuição na superfície de permeabilidade para Tirunelveli e também para quase todas as classes de UCT. Os valores de LST mostraram um aumento global da temperatura da superfície em Tirunelveli, sendo o maior aumento para as áreas urbanas entre 2007 e 2017. O LST também apresentou uma forte associação negativa com o SAVI. As áreas urbanas do Sudeste de Tirunelveli foram representadas como um potencial ponto quente, com uma chamada de atenção para a zona ribeirinha ocidental onde foi verificada a emergência de uma ilha de calor em 2017. Os nossos resultados fornecem métricas importantes sobre a permeabilidade da superfície, temperatura e monitoramento de ilhas de calor e informam as autoridades de planeamento sobre as vantagens da fusão de imagens de satélite. No capítulo 5, identificamos os fatores de degradação dos mangais a nível regional e global resultantes de décadas de dados de investigação (de 1981 até o presente) de variações climáticas (aumento do nível das águas do mar, tempestades, precipitação, eventos extremos de água e temperatura) e atividades humanas (poluição, extração de madeira, aquacultura, agricultura e expansão urbana). Estas informações podem ser úteis para investigações futuras sobre mangais e para ajudar a delinear estratégias de planeamento global que considerem o valor ecológico e económico dos mangais, protegendo-os de novas perdas

Spatial analysis and modelling of fire severity and vegetation recovery on and around Mt Cooke, south-western Australia

The South Western Australian Floristic Region (SWAFR) is an area with high biodiversity and species endemism. Numerous granite outcrops within the area provide specialised ecosystems for these endemic plants that are under threat by changes to the fire regime. This study reviews a fire on Mt Cooke in 2003. Using remote sensing and GIS, the fire is studied in relation to vegetation and fire indices to assess the fire severity and studies if the topography affected the fire severity. The vegetation recovery is monitored for ten years post-fire to assess recovery rates

Lake Area Change In Alaskan National Wildlife Refuges: Magnitude, Mechanisms, And Heterogeneity

Thesis (Ph.D.) University of Alaska Fairbanks, 2011The objective of this dissertation was to estimate the magnitude and mechanisms of lake area change in Alaskan National Wildlife Refuges. An efficient and objective approach to classifying lake area from Landsat imagery was developed, tested, and used to estimate lake area trends at multiple spatial and temporal scales for ~23,000 lakes in ten study areas. Seven study areas had long-term declines in lake area and five study areas had recent declines. The mean rate of change across study areas was -1.07% per year for the long-term records and -0.80% per year for the recent records. The presence of net declines in lake area suggests that, while there was substantial among-lake heterogeneity in trends at scales of 3-22 km a dynamic equilibrium in lake area may not be present. Net declines in lake area are consistent with increases in length of the unfrozen season, evapotranspiration, and vegetation expansion. A field comparison of paired decreasing and non-decreasing lakes identified terrestrialization (i.e., expansion of floating mats into open water with a potential trajectory towards peatland development) as the mechanism for lake area reduction in shallow lakes and thermokarst as the mechanism for non-decreasing lake area in deeper lakes. Consistent with this, study areas with non-decreasing trends tended to be associated with fine-grained soils that tend to be more susceptible to thermokarst due to their higher ice content and a larger percentage of lakes in zones with thermokarst features compared to study areas with decreasing trends. Study areas with decreasing trends tended to have a larger percentage of lakes in herbaceous wetlands and a smaller mean lake size which may be indicative of shallower lakes and enhanced susceptibility to terrestrialization. Terrestrialization and thermokarst may have been enhanced by recent warming which has both accelerated permafrost thawing and lengthened the unfrozen season. Future research should characterize the relative habitat qualities of decreasing, increasing, and stable lakes for fish and wildlife populations and the ability of the fine-scale heterogeneity in individual lake trends to provide broad-scale system resiliency. Future work should also clarify the effects of terrestrialization on the global carbon balance and radiative forcing

Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platformfacilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platformwas launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 andMay 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.Peer ReviewedPostprint (published version

An empirical study of image processing methods for land cover classification and forest cover change detection in Northeastern Oregon\u27s timber resource-dependent communities (1986-2011)

A study was performed to evaluate remote sensing methods for classifying land cover and land cover change throughout a two-county area in Northeastern Oregon (1986-2011). In the past three decades, this region has seen significant changes in forest management -- changes that can be readily identified from the synoptic perspective. This study employs an accuracy assessment-based empirical approach to test a number of advanced digital image processing techniques that have recently emerged in the field of remote sensing. The accuracies are assessed using traditional and area-based error matrices. It was determined that, for single-time land cover classification, Bayes pixel-based classification using samples created with segmentation parameters of scale 8 and shape 0.3 resulted in the highest overall accuracy. For land cover change detection, it was determined that Landsat 5 TM band 7 with a change threshold of 1.75 SD resulted in the highest accuracy for forest harvesting detection